text:
\section{Introduction}
Layout of road networks is essential for diverse applications in geographic information systems.
Efficient reconstruction from images and timely updates of road networks are important both for map designs and handling events such as natural disasters. The availability of high-resolution satellite images has enabled such technology in recent years though the process is not fully automatic.
Currently the road extraction from satellite images is mainly completed manually \cite{van2018spacenet}.
Doing so automatically or even semi-automatically in a reliable manner is challenging as there are a variety of different types of roads whose images are cluttered with noise and occlusions (by cars/trees etc).

Extracting lane-related information from high resolution satellite images has been addressed in recent years~\cite{gu2015fusion,sun2018combining,zang2017lane}.
Specifically for road extraction, a range of methods that combine machine learning and computer vision methods have been proposed to reconstruct roads using labelled data. These are semi-automatic in the sense that they use manually curated samples to train the classifier.
These methods often consist of two main stages.
The first stage consists of the background segmentation and the second stage consists of the centerline extraction.
The background segmentation is usually done via machine learning methods
such as performing feature extraction and  pixel-wise label predictions with SVM \cite{das2011use,shi2014integrated} or CNN \cite{ciresan2012deep}.
More recently, a CNN framework called U-Net \cite{ronneberger2015u} is proposed that outputs the segmentations directly,
improving the predictions and the running time significantly.
The baseline algorithms for SpaceNet Challenge \cite{van2018spacenet} use the architecture such as U-Net  and PSPNet \cite{zhao2017pyramid}.
For the second stage, methods like skeleton or medial axis extraction with pre- and post-processing are often used to obtain the final road networks.
However, recovering the correct connections and junctions of roads still remain challenging.
This problem is critical since the road network is often used in routing and false breaks in the extraction lead to unacceptable results. This two-stage approach
can potentially be fully automatized if training samples can be produced algorithmically.

To achieve the full automatization, one needs to have a direct reconstruction from images that may not be completely faithful but reliable enough to serve as the generator of good training samples. Then, one can iteratively use the technique to improve upon the training samples. This is what we achieve in this work for automatic road reconstruction. It turns out that our direct reconstruction method even improves over the state-of-the-art techniques for semi-automatic reconstructions by providing a more robust algorithm for the second stage. Our direct reconstruction method is a topology-based graph reconstruction algorithm.
It uses the recent techniques of topological
persistence~\cite{EH10} and discrete Morse theory~\cite{forman} in topological data analysis.
This topology-based approach for recovering hidden structures has been proposed and studied recently \cite{DRS15,GDN07,RWS11,weiss2013primal}.
It has been applied to extracting graph-like structures from simulated dark matter density fields \cite{2011MNRAS} and reconstructing road networks from GPS traces \cite{wang2015efficient, dey2017improved}.
This discrete-Morse based graph reconstruction framework is clean both conceptually and implementation-wise. Most importantly, as it uses a global topological structure to make decisions (instead of using purely local information to decide whether a point is on or off the road), the algorithm is robust to noise, non-uniform sampling of the data, and reliable at recovering junctions.
Very recently, this graph reconstruction algorithm has been further simplified, and theoretical guarantees of this graph reconstruction algorithm for the case when the signal prevails noise have been provided  \cite{dey2018graph}.

\paragraph{Specific contributions:}
Our contribution is twofold.\\

(1) First, in a semi-automatic framework, we apply the discrete-Morse based graph reconstruction algorithm on the segmented satellite images obtained by a CNN. This, of course, requires user provided training samples to train the CNN.
We show that this leads to reconstructions with better network connectivity and less noise compared to some existing state-of-the-art technique.

(2) More importantly, next, in a fully automatic framework, we develop a novel method to leverage the power of the discrete-Morse based graph reconstruction algorithm to train a CNN from a collection of images {\bf without labelled data} so that it can produce segmentation for new images.
To elaborate, we start with running the graph reconstruction algorithm on the raw satellite images to obtain some initial reconstructions.
We then put the pixels from reliable branches of the output graph as positive and others as negative to create the labels for the training, and produce an intermediate CNN classifier.
We predict the segmented images for the training set using this intermediate CNN and then repeat the same process on the output to gradually improve the CNN.
Our experiment shows that after several iterations of training, the labels computed from the graph reconstruction algorithm become less noisy and the performance of the classifier improves significantly.
If we relax the condition slightly and assume that we know the labels for only 10\% of the train set, we can incorporate this partially labelled data into our framework, and the performance of the classifier becomes even better.

We experiment on datasets from SpaceNet Challenge \cite{van2018spacenet} which consists of high resolution images for four cities.
For the semi-automatic framework, we compare our results with the results of the winner's algorithm, using the APLS score (defined in  \cite{van2018spacenet}) as well as another metric which we call \hmm{a}verage Hausdorff distance\xspace, to evaluate the quality of the reconstructed networks compared to the ground-truth (provided by SpaceNet Challenge).
Overall, our reconstructions tend to have better connectivity and are less noisy.
For the fully automatic framework, we show that the reconstruction quality is significantly improved through our iterative training process.
Furthermore, our framework can be modified to include a small set of labelled data
and the accuracy improves as we use more and more labelled data.

This paper is organized as follows, Section \ref{sec:preliminary} briefly describes the idea of the discrete-Morse based graph reconstruction algorithm.
Section \ref{sec:aprch}  introduces our semi-automatic framework and fully automatic framework.
Section \ref{sec:exp} provides various experiment results for both frameworks and discusses limitations and future works.

\section{Discrete-Morse based graph reconstruction}
\label{sec:preliminary}

\begin{figure}[t]
\includegraphics[width=\textwidth]{fig/pipeline_overview}
\caption{High level road network reconstruction from satellite images framework pipeline.
\label{fig:pipeline}}
\end{figure}

On the high level, the road network reconstruction from satellite images framework has two stages; see Figure \ref{fig:pipeline}.
In Stage 1, we use some machine learning techniques to convert a given satellite image into a segmented image where roughly speaking, the value at each pixel represents the likelihood of this pixel being on / around roads.
In Stage 2, we extract the hidden road-network (graph) from this segmented image.

We apply the simplified version of our discrete-Morse based graph reconstruction algorithm \cite{dey2018graph} to extract road networks from the segmented images (sometimes called ``road masking'' in the literature).
Given that our approach mostly uses this reconstruction algorithm as a black box, we only provide a high-level description of the main ideas here. Interested readers should see \cite{dey2018graph} for more details.

\begin{figure}[thbp]
    \centering
    \begin{subfigure}[b]{0.4\textwidth}
        \includegraphics[width=\textwidth]{fig/only_terrain.png}
        \caption{}
        \label{fig:terrain:seg}
    \end{subfigure}
     \begin{subfigure}[b]{0.4\textwidth}
        \includegraphics[width=\textwidth]{fig/low_threshold.png}
        \caption{}
        \label{fig:terrain:ridges}
    \end{subfigure}
    \caption{(a) An input density field on the plane (top corner) and its terrain view. (b) Mountain ridges of the terrain (black lines) capture the road network.

    \label{fig:terrain}}
\end{figure}

Given a segmented image, we view it as a density field defined on a 2D grid, where the function value at each vertex reflects
the likelihood of the corresponding pixel belonging to the road class.
The goal is to extract a graph that represents the hidden road network.

In particular, for simplicity, assume we have a triangulation $K$ of the grid (image), and thus the segmented image can be viewed as a ``density function'' $f: K \to \mathbb{R}$ with $f(s)$ reflecting how likely $s$ is in the road class.
The graph of this density function $f$ can be viewed as a terrain with the height of a point $s$ being its function value $f(s)$;
see Figure \ref{fig:terrain:seg}.
Algorithm \WWLAlgSimp() of \cite{dey2018graph} (developed based on earlier work, e.g, \cite{DRS15,GDN07,2011MNRAS,wang2015efficient}) proposes to use the ``mountain ridge" of this terrain to describe the hidden graph.
Intuitively, the mountain ridge structures are formed by those flow lines (following the steepest descending direction) that connect maxima and saddles of this terrain. Curves in the mountain ridges connect mountain peaks and saddles, and separate different ``valleys". A point on such a curve has a higher function value than points off the curve in a direction orthogonal to the curve locally. This is consistent with what a ``road" should be: points in a road have higher ``density'' than points off the road in the orthogonal direction though  this point may not have the highest density value along the road itself. See Figure \ref{fig:terrain:ridges}.

Algorithm \WWLAlgSimp() extracts the ``mountain ridges" from the input density function (terrain) via the so-called \emph{1-stable manifolds} from Morse theory. For the sake of efficient and numerically stable computation,
it uses the discrete Morse theory \cite{forman} to implement it.
Very importantly, the algorithm also uses the concept of \emph{persistent homology} \cite{ELZ02} to capture ``importance'' of different pieces of 1-stable manifolds (more precisely, important max-saddle pairs) in a meaningful manner.
This allows the algorithm to remove noise and simplify the output graph (road network) systematically.

Notice that, since this algorithm uses the global ``mountain ridges'' to infer the hidden networks, it does not need to identify the junction nodes separately, and it can also bridge through small gaps in the density field. The algorithm is clean (uses only one parameter) and efficient. It takes $O(n\log n)$ time for a planar triangulation with $n$ vertices.

\section{Approaches}
\label{sec:aprch}
\subsection{Semi-automatic framework}
\label{sec:semi_auto}
The semi-automatic framework follows the high-level two-stage approach as outlined in Figure \ref{fig:pipeline}.
In the first stage, we train a CNN using training images consisting of ground-truth roads labeled.
Given a raw satellite image, we feed it to this trained CNN to obtain a segmented image.
In the second stage, we apply the discrete-Morse based graph reconstruction algorithm to extract the road-network from the segmented image.
For the second stage to work more accurately, we need to detect road ends called ``tips" in the segmented images obtained in the first stage.
We take advantage of the CNN to add a simple ``tip-detection'' stage that enhances the segmented images.
The overall pipeline for Stage-1 of the semi-automatic framework is shown in Figure \ref{fig:semiautomatic}.
The inputs for the framework are high resolution satellite images, which are split into a test set and a train set.
The train set has ground truth graphs (obtained manually) that represent the centerlines of the road networks.
The road-labels for training are created by thickening the ground truth graph and labeling pixels inside the thickened graph as positive and others as negative.

\begin{figure}[t]
\includegraphics[width=\textwidth]{fig/pipeline_semi_stage1}
\caption{The pipeline for Stage 1 (CNN training) for our semi-automatic framework.
\label{fig:semiautomatic}}
\end{figure}

\paragraph{CNN architecture}
We use the architecture from the winner's approach of the Spacenet Challenge \cite{buslaev2018fully}.
It uses  resnet34 \cite{he2016deep} as encoder and unet-like \cite{ronneberger2015u} decoder.

\paragraph{Reconstructing tips.}
The graph reconstruction algorithm \WWLAlgSimp() sometimes may miss hanging branches.
To remedy this, we propose a novel way to enhance the segmented images. In particular, following the edit strategy of \cite{dey2017improved}, we modify the density values (i.e, the pixel values of the segmented images) of the tips to high values thus causing them to become local maxima which in turn forces reconstructed roads  connecting to them.
We develop two techniques to detect the tips:
(1) Learn the locations of the tips with the same CNN architectures.
(2) Detect the tips from the segmented images by checking the windows around points with high densities.
As shown in Figure \ref{fig:semiautomatic}, we add up the segmented image and the two tip enhancements to obtain the final segmented image to feed to Stage 2.
Figure \ref{fig:tip-cmp} shows the comparison between reconstructions without and with tip enhancements.

\begin{figure}[H]
    \centering
    \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/AOI2/629_ORI}
        \caption{}
        \label{}
    \end{subfigure}
    ~

    \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/AOI2/629_notip}
        \caption{}
        \label{}
    \end{subfigure}
    ~

            \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/AOI2/629_tip}
        \caption{}
        \label{}
    \end{subfigure}
    \caption{Comparison of results without and with tip enhancements. Left: raw satellite images (yellow graphs are ground-truth road networks). Middle/Right: red-graphs are reconstructions without/with tip enhancement respectively, overlaid on top of the ground-truth graph (yellow). Dark colors are the learned density field. }\label{fig:tip-cmp}
\end{figure}

\subsection{Fully Automatic Framework}
\label{sec:ful_auto}

The ground truth labeling used in the semi-automatic framework is itself a graph like structure.
In this section, we propose to create the labels using the discrete-Morse based graph reconstruction algorithm  without the knowledge of the ground truth.
These labels are used to train
a CNN for image segmentation. The segmented images are again labeled by the output of the graph reconstruction algorithm and fed to the CNN for training purpose. A few iterations of training and labeling improves the quality of the image segmentation significantly as our experiments show.
This framework is particularly useful when there is no or very few labelled data to begin with.

\begin{figure}[t]
\includegraphics[width=\textwidth]{fig/pipeline_auto_stage}
\caption{Pipeline for Stage 1 (CNN training) for our fully automatic framework. Note that no input satellite image has labels for roads!
\label{fig:pipeline_auto}}
\end{figure}

Our framework can deal with the following two scenarios.
We perform {\bf label-free learning} when we do not have ground truth roads for any input satellite images to begin with.
We perform {\bf partially-labeled learning} when we have a small fraction of images (say $10\%$ of training set) with road labels.

\paragraph{Label-free case.}
We describe the framework for the label-free case, and the partially-labeled case can be handled by a slight modification of it.
The high-level pipeline of Stage 1 (training a CNN for segmenting an input image) is in Figure \ref{fig:pipeline_auto}.
Given an input set of raw satellite images (with no labels), we split it into the training and testing sets, denoted by $I_0^{tr}$ and $I_0^{test}$, respectively.
We run algorithm \WWLAlgSimp() on each image from $I_0^{tr}$, and let $\Ghat$ be its corresponding output. We use a large threshold for simplification in algorithm \WWLAlgSimp() so as to generate a reconstruction of the more reliable part of the input.
Then we label pixels on $\Ghat$ as positive and pixels on the complement of $\Ghat$ as negative.
Next we train the CNN classifier with those labeled pixels, and this is our first classifier $C_0 = \MorseTrain(I_0^{tr})$ (shown in Algorithm \ref{alg:morselabel}).

\RestyleAlgo{boxruled}
\LinesNumbered
\begin{algorithm}[hptb]
\caption{\MorseTrain($I$) \label{alg:morselabel}}
\DontPrintSemicolon
\KwData{Images $I$}
\KwResult{Classifier $C$}
\Begin{
Compute the triangulation $K$ of $I$ and take pixel values as density function $\rho$\;

$\Ghat$ = \WWLAlgSimp($K$,$\rho$, $\delta$)\;

Label pixels on $\Ghat$ as positive, pixels on the complement of $\Ghat$ as negative\;

Train a CNN classifier $C$ by above features\;

\textbf{return} $C$\;
}
\end{algorithm}

Now feeding each original training image from $I_0^{tr}$ to $C_0$ returns a collection of segmented images $I_1^{tr}$, where in each image, every pixel has a value reflecting the likelihood of it being positive (on the road).
We repeat the steps with images in  $I_1^{tr}$ and obtain a new CNN classifier $C_1 = \MorseTrain(I_1^{tr})$.
In a generic $i$-th iteration of this process, feeding the training images $I_i^{tr}$ to $C_i$ returns segmented images $I_{i+1}^{tr}$, which we use to train a new CNN classifier $C_{i+1} = \MorseTrain(I_{i+1}^{tr})$.
The process terminates when the segmented images $I_i^{tr}$ undergo little changes over iterations.

\paragraph{Partially-labelled case.}
For this scenario, we start training the CNN classifier using only
the labelled training images to obtain $C_0$.
In each of the subsequent iteration $i > 0$, we use both the labels computed from the segmented images $I_i^{tr}$ at this iteration, as well as the original labels from the ground truth.

\section{Experiments}
\label{sec:exp}
\paragraph{Datasets}
We consider data from the SpaceNet Challenge 3 \cite{van2018spacenet}.
It includes four cities: Las Vegas, Paris, Shanghai and Khartoum and consists of the original panchromatic band, the 1.24m resolution 8-band multi-spectral 11-bit geotiff, and a 30 cm resolution Pan-Sharpened 3-band and 8-band 16-bit geotiff.
We only use the 30 cm resolution Pan-Sharpened 3-band (RGB) 16-bit geotiff in our experiments.
Each image from the dataset covers 400m by 400m with a size of 1300px by 1300px.
The ground truth for each image is a graph representing the centerline of the roads.
The width of the roads in the masks is 4 meters.
To evaluate the results, we need to compare the proposed graphs with the ground truth.
So we only take the train set from this challenge (since ground truth is only known for this set).

\paragraph{Metrics.}
The first metric we use to evaluate the results is the Average Path Length Similarity (APLS) \cite{van2018spacenet}. This is the metric used for evaluation in SpaceNet Challenge 3.

\begin{definition}
Let $G_1=(V_1,E_1)$ and $G_2=(V_2,E_2)$ be two input graphs.
For $a,b\in V_1$ where $path(a,b)$ exists in $G_1$,
let $a'$ (resp. $b'$) denote the closet node to $a$ (resp. to $b$) in $G_2$.
$L(\cdot,\cdot)$ denote the length of the shortest path.
First we define the cost of $path(a,b)$:
\[ c(a,b)=
\begin{cases}
      min\left\{  1,\frac{|L(a,b)-L(a',b')|)}{L(a,b)}\right\}, & if \:path(a',b')\: exists\\
      1, & otherwise \\
   \end{cases}
\]
We next define
\begin{equation*}
C(G_1,G_2) = 1- \frac{1}{N}\sum c(a,b)
\end{equation*}
Where $N$= \# unique paths in $G_1$,
and we take the sum over all unique paths.
Finally, the \emph{APLS score of $G_1$ and $G_2$} is defined to be the  harmonic mean of $C(G_1,G_2)$ and $C(G_2,G_1)$:
\begin{equation*}
APLS(G_1,G_2)= \frac{2}{\frac{1}{C(G_1,G_2)}+\frac{1}{C(G_2,G_1)}}
\end{equation*}
\end{definition}

This metric sums the differences in optimal paths existing between nodes in the ground truth graph $G_1$ and the reconstructed graph $G_2$.
It consists of two parts:
part 1 considers optimal paths from the ground truth graph, finds paths from the reconstructed graph which correspond to them and measure differences;
and part 2, in opposite direction, considers paths from the reconstructed graph, finds their correspondences in the ground-truth and compare them.
The final score is the harmonic mean of these two parts.
It cares about the connections between the nodes and punishes breaks in the roads.
However, this metric may not be accurate when the size of the graph and the total amount of paths are small since the metric evaluates the portion (ratio) of paths that match well.
In this case, a small difference in the graphs could result in a relatively large difference in the score (see Figure \ref{fig:mt}).
To obtain a more comprehensive picture, we also use the following \hmm{a}verage Hausdorff distance\xspace:
\begin{definition}\label{def:hau-normal}
Suppose $G_1$ and $G_2$ are two graphs; $P_1$ is the point set sampled from $G_1$; $P_2$ is the point set sampled from $G_2$,
and $d$ denotes the Euclidean distance. Then, the one-directional Hausdorff distance is:
\[ S_{HA}(G_1,G_2):=
\begin{cases}
      \frac{1}{|P_1|}\sum_{p\in P_1}d(p,P_2), &G_1\neq\emptyset \;and\; G_2\neq\emptyset\\
      MAX, & Only \;one\; graph\; is\; empty\\
      0, & G_1=G_2=\emptyset
   \end{cases}
\]
Here, MAX is a specific maximum value.
We set  $S_H(G_1, G_2) = S_{HA}(G_1,G_2)+ S_{HA}(G_2,G_1)$ as final \emph{\hmm{a}verage Hausdorff distance\xspace} between $G_1$ and $G_2$.
\end{definition}

Note that for APLS score, the higher the score is, the more similar the two graphs are.
But for \hmm{a}verage Hausdorff distance\xspace, the lower the distance is, the more similar the two graphs are.

\begin{figure}[htbp]
    \centering
        \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/AOI2/1634_ORI}
        \caption*{AOI\_2-Id: 1634}
        \label{}
    \end{subfigure}
    \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/AOI2/1634}
        \caption*{0.8560 / 14.0662}
        \label{}
    \end{subfigure}

    \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/AOI2/1634_L0-4_U3}
        \caption*{0.4684 / 15.3238}
        \label{}
    \end{subfigure}
    \caption{\label{fig:mt}Left: input satellite image. Middle/Right: reconstruction of Buslaev's method and our method. The APLS-score/\hmm{a}verage Hausdorff distance\xspace are listed. Note that even though our reconstruction is very similar, the APLS-score is significantly lower due to the sparsity of the signal. \hmm{a}verage Hausdorff distance\xspace is more accurate for this case.}
\end{figure}

\paragraph{Parameters. }
There are several parameters in the entire pipeline, among which the persistence threshold $\delta$ (for the discrete-Morse based reconstruction algorithm) and the arc-intensity threshold $\tau$ (used to further remove noisy arcs during the post-processing) affect the results most.
To tune these two hyperparameters, we experiment on the validation set with a range of parameters that are chosen empirically, then take the set of parameters that give the highest score.
We take APLS scores as the reference to tune the parameters.

Furthermore, for datasets AOI\_3 (Shanghai) and AOI\_4 (Paris), there are many images with extremely sparse signal, while many of them have much denser signal. We thus use a two-threshold system for the arc-intensity threshold:
For those images we need a low arc-intensity threshold $\tau$: We sort the images by the sum of their intensities, and apply a lower $\tau$ to those images with low total-intensity. We use a higher $\tau$ for the remaining images.
\begin{wrapfigure}{r}{3cm}
        \includegraphics[height=2.5cm]{fig/AOI4/Lpartition1_valid_arch_sumvalue}
\end{wrapfigure}
For example, see the right figure for dataset AOI\_4, where $x$-axis is the percentage of images (sorted in increasing total-intensity), and $y$-axis is their total-intensity. Given that there is a sharp transition around $20\%$, we apply a lower threshold to the $20\%$ of images with the lowest total intensity. We use the same strategy for AOI\_3, and choose $40\%$ as the threshold to have two $\tau$ values.

\subsection{Semi-automatic reconstruction results}
\label{sec:semi-exp}
\noindent

\begin{table}[htbp]
\begin{center}
  \begin{tabular}{| l | l | c | l | l |}
    \hline
     & train & validation& test & total\\ \hline
    AOI\_2\_Vegas & 659 &165 &165& 989\\ \hline
    AOI\_3\_Paris &206  & 52 &52&310\\ \hline
    AOI\_4\_Shanghai & 798 & 200 &200&1198\\ \hline
    AOI\_5\_Khartoum &189 & 47 &47&283\\ \hline
          \end{tabular}
          \caption{The split of the dataset.The ratio for train/test/valid data is approximately 4:1:1.}\label{tab:split}
\end{center}
\end{table}

\begin{table}[htbp]
\centering
\begin{tabular}{|l|l|l|l|l|}
\hline
     & \multicolumn{2}{l|}{APLS}                                           & \multicolumn{2}{l|}{$S_H$} \\ \hline
     & Buslaev\cite{albu}                           & ours                             & Buslaev\cite{albu}        & ours       \\ \hline
AOI\_2 & 0.8211 & \textbf{0.8278}                          &18.3539               &\textbf{17.7841}            \\ \hline
AOI\_3 & 0.5848 & \textbf{0.6324}                          &291.0188               & \textbf{289.9532}           \\ \hline
AOI\_4 & 0.6630                           & \textbf{0.6632} &69.5775               &\textbf{68.9596}            \\ \hline
AOI\_5 & 0.6069                           & \textbf{0.6477} & 44.4201              &\textbf{41.6037}            \\ \hline
\end{tabular}
\caption{APLS score and \hmm{a}verage Hausdorff distance\xspace for the test set. The $MAX$ value used for \hmm{a}verage Hausdorff distance\xspace is 500 pixels, the size of each image is 1300px by 1300px. The \hmm{a}verage Hausdorff distance\xspace for AOI\_3 is high because there are more cases where the proposed graph is empty while the ground truth graph is not.}
\label{tab:two_scores}
\end{table}

\begin{table}[htbp]
\centering
\begin{tabular}{|l|l|l|}
\hline
       & $\delta$ & $\tau$ \\ \hline
AOI\_2 &0.12          & 0.4       \\ \hline
AOI\_3 & 0.1         &0.3(30\%)/0.4        \\ \hline
AOI\_4 &  0.1        & 0.3(40\%)/0.4       \\ \hline
AOI\_5 &  0.07        & 0.3       \\ \hline
\end{tabular}
\caption{Chosen parameters. For AOI\_3, the content in the column $\tau$ means that for 40\% of images with the lowest total intensity, take $\tau=0.3$ and for the rest of the images take $\tau=0.4$. The same for AOI\_4.}
\label{tab:para}
\end{table}

\paragraph{Compared method: Buslaev's method \cite{albu}}
We compare our framework with the method of the winner of SpaceNet Challenge 3 \cite{albu}.
It uses the same CNN architecture to train and then predict the segmented images.
In Stage 2, Buslaev's method first extracts the skeleton from the thresholded segmented images.
Then, it transforms the skeleton to a multi-graph using library ``sknw'' \cite{sknw}. Finally, it translates the multi-graph to a graph with straight edges.
Buslaev's method outperforms other methods in SpaceNet Challenge 3, so we only compare ours with this one.

\paragraph{Results.}
As mentioned before, we tested on four datasets. The split of train-validation-test is 4:1:1 for each data set, and the precise  numbers are listed in Table \ref{tab:split}.

Tables \ref{tab:two_scores} shows the scores under the two metrics for Buslaev's framework and ours over test datasets (on validation datasets our scores are consistently better).
Each score is an average of scores for all test images (recall the split of train-validation-test is shown in Table \ref{tab:split}).
For APLS score, the larger value the better it is.
For \hmm{a}verage Hausdorff distance\xspace, the smaller value the better it is.
Note that AOI\_4 and AOI\_5 are rather noisy images (especially AOI\_5) and most challenging among all datasets.
Our method significantly outperforms Buslaev's method on AOI\_5.
We also observe that, in general, our output tends to have better connectivity.
Figure \ref{fig:reconexamples} shows a few examples.
Buslaev's algorithm tends to have more extra branches, and worse connectivity.
We note that the final average APLS score reported here for Buslaev's method is different from the posted one 0.6663 in \cite{spacenetResult}. This is because the posted score is computed for the original test set from SpaceNet challenge, while we our test set is a subset of the original train set -- we cannot compare on the original test set from SpaceNet challenge as the ground truth for them are not publicly available.
Tables \ref{tab:para} shows the finally chosen parameters for the reproducibility of the experiment.

\paragraph{Running time.}
For each of the two larger datasets (AOI\_2\_Vegas and AOI\_3\_Shanghai): Training takes around 500 minutes to learn both the lanes and the tip-marks. Testing (to obtain the segmentation on all images from the test-set) takes around 18 minutes. The final road network extraction stage takes 20 mins for each choice of two hyper-parameters (persistence threshold and intensity threshold), and total 20 * 9 = 180 minutes to tune the two hyper parameters on validation sets and then run on the final testing sets. We note that the graph reconstruction code is not optimized and we believe can be improved for the 2D setting, which would improve the time for the last final road network extraction stage.

For each of the two smaller datasets (AOI\_3\_Paris and AOI\_5\_Khartoum): Training takes around 140 minutes to learn both the lanes and tip-marks. Testing (to obtain the segmentation on all images from the test-set) takes around 6 minutes. The final road network extraction stage takes 6 mins for each choice of parameter set, and total 54 minutes to tune the two hyper parameters on validation sets and then run on the final testing sets.

\begin{table}[htbp]
\centering
\small\addtolength{\tabcolsep}{-3pt}
\begin{tabular}{|c|c|c|c|}
\hline
       & Train & Test & Road extraction \\ \hline
AOI\_2 & $2\times 242m$   & $8m\times 2$   & $22m\times 9$             \\ \hline
AOI\_3 & $77m\times 2$   & $2m\times 2$   & $6m\times 9$              \\ \hline
AOI\_4 & $293m\times 2$  & $10m\times 2$  & $27m\times 9$             \\ \hline
AOI\_5 & $71m\times 2$   & $2m\times 2$   & $6m\times 9$              \\ \hline
\end{tabular}
\caption{The running time: $m$ stands for \emph{minutes}; $2\times$ means it will be run twice (for both road and tip detections). $9\times$ comes from the tuning of the parameters.}
\label{tab:time}
\end{table}

\begin{figure}[htbp]
    \centering
 \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/AOI2/1462_ORI}
        \caption*{AOI\_2-Id: 1462}
        \label{}
    \end{subfigure}
    ~

    \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/AOI2/1462}
        \caption*{0.5700 / 18.6828 }
        \label{}
    \end{subfigure}
    ~
        \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/AOI2/1462_L0-4_U3}
        \caption*{0.6655 / 14.2246  }
        \label{}
    \end{subfigure} \\
    \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/AOI3/217_ORI}
        \caption*{AOI\_3-Id: 217}
        \label{}
    \end{subfigure}
    ~

    \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/AOI3/217}
        \caption*{0.6194 / 20.7057 }
        \label{}
    \end{subfigure}
    ~
        \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/AOI3/217_L0-4_U3}
        \caption*{0.8193 / 16.8086  }
        \label{}
    \end{subfigure}
    \\
    \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/AOI4/267_ORI}
        \caption*{AOI\_4-Id: 267}
        \label{}
    \end{subfigure}
    ~

    \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/AOI4/267}
        \caption*{0.4721 / 29.5151 }
        \label{}
    \end{subfigure}
    ~
        \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/AOI4/267_L0-4_U3}
        \caption*{0.5693 / 21.6930  }
        \label{}
    \end{subfigure}
    \\
       \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/AOI5/207_ORI}
        \caption*{AOI\_5-Id: 207}
        \label{}
    \end{subfigure}
    ~

    \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/AOI5/207}
        \caption*{0.6334 / 30.0484 }
        \label{}
    \end{subfigure}
    ~
        \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/AOI5/207_L0-3_U3}
        \caption*{0.7287 / 24.9911  }
        \label{}
    \end{subfigure}
    \caption{Left: raw satellite images (yellow graphs are ground-truth road networks). Middle/right: red-graphs are reconstruction of Buslaev's method and our semi-automatic framework, respectively, overlaid on top of the ground-truth graph (yellow). Dark colors are the learned density field. The numbers given are APLS-score/\hmm{a}verage Hausdorff distance\xspace.  \label{fig:reconexamples}}
\end{figure}

\subsection{Fully automatic reconstruction results}

In the following experiments, we randomly select 200 images as the training set $I_0^{tr}$, and 50 images as the test set $I_0^{test}$ for each dataset.
We evaluate the method by computing the APLS scores on the original test set after each iteration.
We initialize our fully automatic approach by converting each RGB image to grayscale and then applying a Gaussian filter.
One could potentially use other image processing methods to further pre-process it.
When applying the graph reconstruction algorithm, we use the same parameters used in Section \ref{sec:semi-exp} Table \ref{tab:para}.

\paragraph{Alternative method for centerline detection.}
To show that the discrete-Morse based graph reconstruction algorithm is important for our fully-automatic training framework, we develop the following alternative scheme  \SkeTrain() as a baseline to compare: the graph reconstruction algorithm is replaced with the Buslaev's \cite{albu} skeleton extraction algorithm (as described in Section \ref{sec:semi-exp}).

Note that this skeleton extraction used in \cite{albu} is not designed to work directly on the raw satellite images; see Figure \ref{fig:ske}, where in (a) we show an output by this skeleton extraction algorithm directly applied to a raw satellite image (yellow curves are ground truth), while (b) shows the output of the discrete Morse-based algorithm on the same input,
which is much better.
Hence to improve the performance of this the baseline method \SkeTrain(), we will still first use the discrete-Morse graph reconstruction algorithm (or if there are partially-labelled data, using those first) at the beginning of the training process, and switch to the skeleton extraction algorithm only after a few iterations.

\begin{figure}[htbp]
    \centering
        \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/nogt/ori_train_AOI2_56_albu}
        \caption{ }
        \label{}
    \end{subfigure}
    \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/nogt/ori_train_AOI2_56}
        \caption{}
        \label{}
    \end{subfigure}
    \caption{Left: Skeleton by \cite{albu}. Right: Skeleton by discrete-Morse algorithm.}
    \label{fig:ske}
\end{figure}

\begin{table*}[htbp]
\resizebox{\columnwidth}{!}{%
\begin{tabular}{|l|ll|l|l|l|l|lllll}
\hline
AOI\_2                         & \multicolumn{1}{l|}{$I_1^{test}$} & $ I_2^{test}$ & $ I_3^{test} $ & $ I_4^{test}$ & $I_5^{test}$ & $I_6^{test}$ & \multicolumn{1}{l|}{$I_7^{test}$} & \multicolumn{1}{l|}{$I_8^{test}$} & \multicolumn{1}{l|}{$I_9^{test}$} & \multicolumn{1}{l|}{$I_{10}^{test}$} & \multicolumn{1}{l|}{$I_{11}^{test}$} \\ \hline
\MorseTrain() & \multicolumn{1}{l|}{0.2523}     & 0.3340      & 0.3886       & 0.4173      & 0.4332                         & 0.4655                         & \multicolumn{1}{l|}{0.4829}                         & \multicolumn{1}{l|}{0.5252}                         & \multicolumn{1}{l|}{0.5497}                         & \multicolumn{1}{l|}{0.5813}                          & \multicolumn{1}{l|}{0.5922}                          \\ \hline
\SkeTrain()   &                                 &             & 0.2677       & 0.2643      & 0.2763                         & 0.2753                         &                                                     &                                                     &                                                     &                                                      &                                                      \\ \cline{1-1} \cline{4-7}
\end{tabular}
}
\caption{APLS-score for the reconstructed road networks for testing images, based on our label-free framework (\MorseTrain()), compared to the alternative method \SkeTrain(). The first two iterations for \SkeTrain() is done by \MorseTrain() and thus are not shown. After 6 iterations, the score does not improve for \SkeTrain() any more.
\label{tab:label-free}}
\end{table*}

\begin{figure}[htbp]
    \centering
        \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/nogt/323_ORI}
        \caption*{AOI\_2 - Id: 323 }
        \label{}
    \end{subfigure}
    \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/nogt/323_I1}
        \caption*{$I^{te.}_1$ 0.0653 / 57.0400}
        \label{}
    \end{subfigure}
        \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/nogt/323_I11}
        \caption*{$I^{te.}_{11}$ 0.5564 / 30.3855}
        \label{}
    \end{subfigure}
    \caption{Left: raw satellite image (ground truth in yellow, even though it is not used!). Middle / right: the reconstructed graph using CNN after the first and the 11-th iterations. }
    \label{fig:aoi2_n_gt}
\end{figure}

\paragraph{Results for label-free case.}
We show results here for dataset AOI\_2\_Vegas, which is a cleaner dataset from SpaceNet Challenge. Our new fully-automatic framework is less effective on AOI\_5\_Khartoum, which is much more noisy; however, we will show later that, with 10\% labelled images, it can obtain reasonable results on the challenging AOI\_5\_Khartoum dataset as well.

For test images, we always apply tip detection and arc removal when running the graph reconstruction algorithm.
These two procedures are not applied to the segmented images during the first three iterations of the training process of the pipeline in Figure \ref{fig:pipeline_auto}, as removing arcs results in loss of signals and tip detection tends to introduce noise when the segmented images are not yet reliable.
From $I_4^{tr}$ onward, we start to apply tip detection since the segmented images are now less noisy.
We also decrease the threshold for persistence simplification for the discrete-Morse based graph reconstruction for $i \ge 8$, as the quality of segmented images becomes better and better.

In Table \ref{tab:label-free}, we show the APLS-score for test images using the CNN $C_i$ learned at the $i$-th iterations, as $i$ increases (the \hmm{a}verage Hausdorff distance\xspace shows a similar trend).
In particular, $I_i^{test} (i>0)$ represents the output reconstructed from the segmented images of the set $I_0^{test}$ using the trained CNN $C_{i-1}$.
We compare the output of our framework for label-free case, denoted by \MorseTrain(), with the output of the baseline method \SkeTrain().
Note that, as explained earlier, the first two iterations for \SkeTrain() are done by \MorseTrain() (using discrete Morse graph reconstruction), and thus no APLS-scores are given for those two iterations for \SkeTrain().
Also no APLS-scores is shown for \SkeTrain() after the 6th iterations as the score does not improve further.
In contrast, the APLS-score continues to improve (for test images) during the iterative process.
In Figure \ref{fig:aoi2_n_gt}, we show an example of the reconstructed graph using the CNN from different iterations of our fully automatic training process: observe that at the beginning, only part of signals are captured. Subsequently, the classifier becomes better and more and more signals are captured.

For this set of (200 + 50) images sampled from AOI\_2 dataset, the APLS-score for our semi-automatic framework is about $0.796$. In this fully automatic framework, in the end we obtain a score of $0.592$, which is worse. However, keep in mind that no labels are used at all.

\begin{table}[]
\centering
\begin{tabular}{|l|l|l|l|l|}
\hline
 APLS         & $I_1^{test}$ & $I_2^{test}$ & $I_3^{test}$ & $I_4^{test}$ \\ \hline
AOI\_2 ours  & 0.6521     & 0.6918     & 0.7305     & 0.7210     \\ \hline
AOI\_2 Skel. & 0.4860     & 0.5137     & 0.5214     & 0.5252     \\ \hline\hline
AOI\_5 ours  & 0.5351     & 0.5787     & 0.5893     & 0.6077     \\ \hline
AOI\_5 Skel. & 0.5247     & 0.5091     & 0.4884     & 0.4543     \\ \hline
\end{tabular}
\caption{APLS score for partially-labeled case, where 10\% random images have road labels.}
\label{tab:10gt_APLS}
\end{table}

\begin{figure}[H]
    \centering
                \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/nogt/AOI2_429_ORI}
        \caption*{AOI\_2 - Id: 429}
        \label{}
    \end{subfigure}
    \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/nogt/AOI2_us_rand20_00_429}
        \caption*{$I_1^{te.}$ 0.3926 / 18.3656}
        \label{}
    \end{subfigure}

    \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/nogt/AOI2_us_rand20_33_429}
        \caption*{$I_4^{te.}$ 0.6374 / 15.6549}
        \label{}
    \end{subfigure}
    \\
            \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/nogt/AOI5_50_ORI}
        \caption*{AOI\_5 - Id: 50}
        \label{}
    \end{subfigure}
    \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/nogt/AOI5_us_rand20_00_50}
        \caption*{$I_1^{te.}$ 0.6449 / 68.8017}
        \label{}
    \end{subfigure}

    \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/nogt/AOI5_us_rand20_33_50}
        \caption*{$I_4^{te.}$ 0.6648 / 50.7188}
        \label{}
    \end{subfigure}
    \\
        \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/nogt/AOI5_150_ORI}
        \caption*{AOI\_5 - Id: 150}
        \label{}
    \end{subfigure}
    \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/nogt/AOI5_us_rand20_00_150}
        \caption*{$I_1^{te.}$ 0.3758 / 28.1091}
        \label{}
    \end{subfigure}

    \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/nogt/AOI5_us_rand20_33_150}
        \caption*{$I_4^{te.}$ 0.7356 / 23.2734}
        \label{}
    \end{subfigure}
    \caption{Using 10\% labelled images. Reconstructed graphs by our \MorseTrain() after 1 and 4 iterations.}
    \label{fig:rand20_us}
\end{figure}

\paragraph{With 10\% ground truth}
Now we use a small set of labelled data: Specifically, we assume that only 10\% images (i.e, 20 images) have labels (i.e, ground-truth roads given).
Table \ref{tab:10gt_APLS} shows the APLS-score for test images after different iterations by \MorseTrain() and \SkeTrain().
For \MorseTrain{}, all scores improve.
It is important to note that with only 10\% labeled-images, we can now also handle the challenging AOI\_5 dataset, and achieve an APLS-score of $0.607$. (For the case of AOI\_2, compared to the label-free case, the score of our new \MorseTrain() improves to $0.721$ from $0.592$).

It is interesting to note that this iterative procedure does not seem to help  \SkeTrain() much, with scores even getting worse for the noisy dataset AOI\_5.
We show some examples of reconstructed graphs at different iterations for our algorithm (Figure \ref{fig:rand20_us}) and for the alternative \SkeTrain() method (Figure \ref{fig:rand20_ske}).

\begin{figure}[htbp]
    \centering
            \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/nogt/AOI2_429_ORI}
        \caption*{AOI\_2 429}
        \label{}
    \end{subfigure}
    \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/nogt/AOI2_ske_rand20_00_429}
        \caption*{$I_1^{te.}$ 0.2430 / 16.7267}
        \label{}
    \end{subfigure}

    \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/nogt/AOI2_ske_rand20_33_429}
        \caption*{$I_4^{te.}$ 0.2547 / 11.8099}
        \label{}
    \end{subfigure}
    \\
        \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/nogt/AOI5_50_ORI}
        \caption*{AOI\_5 50}
        \label{}
    \end{subfigure}
    \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/nogt/AOI5_ske_rand20_00_50}
        \caption*{$I_1^{te.}$ 0.4533 / 74.7638}
        \label{}
    \end{subfigure}

    \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/nogt/AOI5_ske_rand20_33_50}
        \caption*{$I_4^{te.}$ 0.1940 / 94.0461}
        \label{}
    \end{subfigure}
    \caption{Using 10\% labelled images. Reconstruction of the alternative \SkeTrain() method after 1 and 4 iterations.}
    \label{fig:rand20_ske}
\end{figure}

\subsection{Limitations and future work}
\label{sec:future_work}
First, currently we choose the parameter $\delta$ globally.
Figure \ref{fig:para-delta} shows the effect of the persistence threshold $\delta$.
The example demonstrates that there is no single parameter value that works for all cases.
As for the parameter arc-intensity threshold $\tau$,
we choose it adaptively for AOI\_3\_Shanghai and AOI\_4\_Paris by the intensity of the images to deal with the extreme sparse images.
For general cases, it is hard to make this choice simply based on the
intensities of the images, see Figure \ref{fig:para-arc}.
An interesting future research direction would be to investigate how to choose
these parameters adaptively,
yet (semi-)automatically.
Second, we recover the tips by locating their positions and modifying the density values.
It will be interesting to see if we can recover the tips from the
graph reconstruction algorithm directly.
Third, we observe that the fully automatic framework sometimes is not efficient for a noisy dataset such as AOI\_5\_Khartoum.  It would be good to improve the performance of this approach for noisy datasets.

\begin{figure}[htbp]
    \centering

    \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/parameters/333_ORI}
        \caption*{AOI\_2-Id: 333}
        \label{}
    \end{subfigure}
    ~

    \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/parameters/delta01_333}
        \caption*{$\delta$=0.1 / 0.8162 }
        \label{}
    \end{subfigure}
    ~
        \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/parameters/delta015_333}
        \caption*{$\delta$=0.15 / 0.6372  }
        \label{}
    \end{subfigure}\\
     \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/parameters/1107_ORI}
        \caption*{AOI\_2-Id: 1107}
        \label{}
    \end{subfigure}
    ~

    \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/parameters/delta01_1107}
        \caption*{$\delta$=0.1 / 0.8412 }
        \label{}
    \end{subfigure}
    ~
        \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/parameters/delta015_1107}
        \caption*{$\delta$=0.15 / 0.9035 }
        \label{}
    \end{subfigure}
    \caption{Effects of the persistence threshold $\delta$ on the results. Left: raw satellite images (yellow graphs are ground-truth road networks). Middle/right: Results for different  $\delta$, the number after $\delta$ is the APLS score. The first row gives an example where low $\delta$ leads to better results and the second row  gives an example where high $\delta$ leads to better results. \label{fig:para-delta}}
\end{figure}

\begin{figure}[htbp]
    \centering
 \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/parameters/964}
        \caption*{AOI\_2-Id: 964}
        \label{}
    \end{subfigure}
    ~

    \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/parameters/arc_0-582719208_AOI2_964_L0-4_U3}
        \caption*{$\tau$=0.4 / 0.5827 }
        \label{}
    \end{subfigure}
    ~
        \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/parameters/arc_0-459892491_AOI2_964_L0-5_U3}
        \caption*{$\tau$=0.5 / 0.4598 }
        \label{}
    \end{subfigure} \\
    \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/parameters/750}
        \caption*{AOI\_2-Id: 750}
        \label{}
    \end{subfigure}
    ~

    \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/parameters/arc_0-925520224_AOI2_750_L0-4_U3}
        \caption*{$\tau$=0.4 / 0.9255 }
        \label{}
    \end{subfigure}
    ~
        \begin{subfigure}[b]{0.3\textwidth}
        \includegraphics[width=\textwidth]{fig/parameters/arc_0-975350343_AOI2_750_L0-5_U3}
        \caption*{$\tau$=0.5 / 0.9753  }
        \label{}
    \end{subfigure}
    \caption{Effects of the arc intensity threshold $\tau$. Left: raw satellite images (yellow graphs are ground-truth road networks). Middle/right: Results for different  $\tau$, the number after $\tau$ is the APLS score. The first row gives example when low $\tau$ leads to better results and the second row  gives example when high $\tau$ leads to better results. \label{fig:para-arc}}
\end{figure}

\vspace{0.1in}
\noindent
{\bf Acknowledgment}: We acknowledge the NSF grants CCF-1740761, RI-1815697, CCF-1733798 and CCF-1618247 for partially supporting this research.

redpajama_set_name: RedPajamaArXiv


On Monday, June 1, 2015, shortly after 1:00 p.m., we lost our leader, Chief Chris Vinson. Chief Vinson passed away due to complications from a heart surgery. All of us are in shock, and have no words to truly express our sadness. Chief Vinson placed priority on his family – please keep Chief Vinson's family in your thoughts and prayer.
Life is precious. We will miss our Chief.
An account has been established for contributions to the Vinson family to offset funeral and medical expenses.


All of the coaches below were recognized for their outstanding contributions to Special Olympics Missouri from their respective areas at the SOMO Annual Awards Luncheon Jan. 17 in Branson. Each year, Special Olympics Missouri salutes those who have made significant contributions to the Special Olympics movement. Each area nominee is submitted for statewide recognition, and the overall winners were also announced at the SOMO Leadership Conference.
Mandie Bowman was an assistant coach for many years and now has progressed to be the liaison for all the schools within the St. Joseph School District. Mandie is one of the most energetic coaches as she has taken her coaching responsibilities a step further by setting up Fan Clubs. She is the voice in the St. Joseph School District when it is budget time, and she helps by recruiting volunteers for YAP. She helps utilize the school district and the facilities more than the North Area program has ever done in the past. Without Mandie's help, we would not be utilizing the SJSD and the facilities as we are today. Mandie was instrumental in starting the T.U.F. Club at Truman Middle School, which stands for Truman United Fans. They hold pep rallies prior to events, decorate the athletes' lockers, help with fundraisers, become Unified Partners and have volunteered at local and area events.
Keith Patterson has been a coach for more than 10 years. He coaches bowling, basketball and athletics for the Neosho School District. Besides being a volunteer coach, he is also a Unified Partner and volunteer official. Keith has been known to do all three in one day if he is needed. He loves watching his athletes fill with joy as they receive their medals. He always has a smile on his face and positive words to say. He continues to grow the school's Unified Sports program every year. Over the past few years, Keith has done an outstanding job helping with the area basketball tournament. He gets the gym space, officials and volunteers. He has also taken it upon himself to get shirts for every athlete who attends. Last summer, Keith coached the Team Missouri women's basketball team at the USA Games in New Jersey. He worked hard to unite athletes from across the state to play as a team; and as a team they proudly brought home the silver medal. Keith is also very involved in the local Polar Plunge.
Jeff Partridge first realized how important Special Olympics is when his son Ryan became involved in the program. He began by helping the Rebels team with basketball, eventually becoming their head coach. Basketball led into softball and softball led into bowling. Jeff has a real desire to see his team improve and excel. With Jeff, there is no off-season. He is a true leader and friend to his athletes. They all look up to him and work hard on and off the court to be the best they can be for him. Jeff is the Chief Engagement Officer of the YMCA of Southeast Missouri. This is a wonderful facility in the Southeast Area and Jeff invites our teams in to practice or work out for whatever season they are preparing for. Jeff has also become instrumental in assisting us with the Young Athletes Program.
As a physical therapist at St. Charles Habilitation Center, Sheri Morris directly sees the social and physical benefits that Special Olympics offers her clients and is constantly pushing them to be involved in as much as possible. She escorts athletes to Blues and Rams games with tickets that have been donated to SOMO, she is at every weekend Field Day that her athletes might enjoy, and she makes sure athletes and staff are at every program event possible. Some of Sheri's clients participate in aquatics with another coach and she's always there cheering on her friends, even though she is not coaching them!
When Dee Peterson first started with Special Olympics, she was a special education teacher and wanted a chance for her students to participate. Time went on and Dee moved to a "regular" education classroom, but not giving up her love for her former students and Special Olympics, she stayed on as the coach for Laquey Schools. Previously, Laquey athletes only competed in the area bowling tournament and the Area Spring Games. Wanting to give them a chance to experience something new, she asked for them to compete in the state bowling tournament. With her help, the Buckhorn Local Bowl was born. What went from a little local bowl has now evolved into a Unified Team Local Bowl! Later, she was interested in basketball, so the Unified 3-on-3 Basketball Tournament was born! In mid-March, small schools from the area put together Unified 3-on-3 teams and come over to Laquey Middle School to compete against each other.
Venessa McCloud began volunteering for Special Olympics winter sports in 1989 while working at Snow Creek in Weston. Moving to Platte County Board of Services (PCBS) in 1992, she saw the need to provide the full range of recreational and athletic opportunities to those who predominantly live in group homes. Twenty-five years later, Venessa is the glue that holds the PCBS Special Olympics program together. She has been instrumental in the development of the SOMO program for PCBS, putting in the extra time and effort required. She is fully qualified as a head coach for basketball, track and field, bowling, golf, swimming, skiing, bocce and snowshoe racing. As a qualified CPR and First Aid instructor, Venessa ensures all coaches and volunteers meet the medical training required to participate in SOMO sports. She has actively recruited, trained and mentored the athletes, parents, volunteers and other coaches in an exceptionally run SOMO program that successfully meets the needs of more than 60 PCBS athletes year-round.
All of the athletes below were recognized for their outstanding contributions to Special Olympics Missouri from their respective areas at the SOMO Annual Awards Luncheon Jan. 17 in Branson. Each year, Special Olympics Missouri salutes those who have made significant contributions to the Special Olympics movement. Each area nominee is submitted for statewide recognition, and the overall winners were also announced at the SOMO Leadership Conference.
Allison D'Agostino doesn't seek the limelight everywhere she goes, but somehow it finds her. It's the nurturing way she shares her skills and knowledge with the other athletes she trains and competes with that sets her apart. It wouldn't be odd to find Allison working with another athlete to help them understand a swim stroke or retrieving their bowling ball. She is a very giving person. At 25 years old, Allison has been able to build up her confidence level through Special Olympics. This confidence brought her from just training in aquatics to competing and gaining in her skill. This same confidence has allowed her to live alone for four years and start not one but two media outlets on her own. Allison has her own YouTube Channel called The Esperanza243 and conducts a radio show on BlogTalkRadio.com called Show Time, Radio Time. These experiences led her to be selected to be trained as a Global Messenger and she had her first speaking engagement at the Polar Plunge in Columbia. As part of a new communications project at SOMO, Allison was selected to be the first host of the brand new SOMO TV Show (SOMO.org/TV). All these things that Allison does are on top of holding a full-time job at Wendy's. She is a model employee and shows her independence by making sure she gets to and from work on time. She is also one to help with the Central Area through fundraising, speaking in front of groups or whatever is needed.
Michael Lunceford is a quiet and unassuming young man who has been participating in Special Olympics for six years. In that time, Michael has grown as a person who is willing to learn, work hard and do everything he can to help his team. Michael plays several sports including basketball, track & field, golf and bocce. His favorite by far though is bowling. Michael is very diligent about coming to practice and working hard. That diligence paid off when Michael was selected for Team Missouri for the 2014 Special Olympics USA Games in New Jersey. While there, he received two gold medals and a 4th place ribbon. Michael's perseverance in Special Olympics has paid off in other areas of Michael's life. While in high school, he won several state awards for his wood working projects. He loves to play the drums and got to play them in a special education production of "The Lion King." Michael is currently a part of a transition program between his school and a local medical center where he is working on job and life skills. He is also currently working with his father volunteering for Missouri Town, an historic village in Jackson County, as a "tin smith." He is able to talk to visitors and tell them exactly what it is they are making and the methods they are using. Special Olympics has given Michael the self-confidence to work hard and be as independent as possible. It has not only taught him athletic skills but also life skills. It has taught him what it means to be a part of a team.
Becca Tincknell has been contributing to Special Olympics Missouri since she first joined the program at the age of seven. She currently competes in bowling, basketball, softball, flag football, athletics and bocce – an all-around athlete! But she is always willing to try new sports: When asked to fill a tennis slot for the 2003 World Games in Dublin, she had never played the sport before but took a year's worth of lessons and returned home with a gold medal. She was also proud to represent Team Missouri at the 2014 USA Games, winning a gold, silver and fourth-place ribbon in bocce. She is trained as a Global Messenger and is a reliable and entertaining speaker and volunteer at events and fundraisers. After competing in the 2014 USA Games as a bocce player, Becca is now participating in bocce practices with her team as an official, allowing her to focus more closely on the game. She not only shares her strategy with her fellow players, but also learns more about the game in the process. She was recently featured in the Jefferson County Leader as its "Athlete of the Week" and was selected as one of two athletes to be on a billboard with Robert Quinn of the St. Louis Rams, promoting their partnership with Special Olympics Missouri.
Lonnie Thornton has been participating in Special Olympics for 13 years, and this past year has been an incredible one for him. Last summer, Lonnie attended the USA Games in New Jersey. He competed in bocce and earned gold, silver and bronze medals. Don't challenge him to a game of bocce unless you are prepared to lose, because he is very good! While at the Games, Lonnie was able to do something he has always wanted to do — see the Statue of Liberty. He said this was the best time of his life since the accident that left him with a disability. Lonnie was also featured on a billboard in his community for his achievements as well as led the Athlete's Oath at the State Outdoor Games. Lonnie previously competed at the 2006 National Games in Iowa. He also competes in basketball, bowling and softball. Lonnie is well known at SOMO and in his community. Staff, coaches, volunteers and other athletes enjoy being around him because he is always friendly, smiling or cracking a joke. Go anywhere with Lonnie in his community and someone he knows always stops to chat or ask how he is doing. He is never shy to express how much he appreciates and loves Special Olympics.
Brianne Chavez has participated in Special Olympics sports since graduating high school in 2005. She has played softball, basketball, soccer, track and powerlifting. In 2008, Brianne received the Southeast Area Outstanding Athlete award along with the discovery of powerlifting. Not only has she done well at State Summer Games competitions, Brianne has set APA (American Powerlifting Association) national and world records for the Special Olympics women's division in 148 lb. and 165 lb. classes as well as 22-23 year old women's division in 2008 and 2009. In 2010, she earned a spot on Team Missouri's USA Games Unified Softball Team. In 2014, she qualified as a powerlifter for Team Missouri, excelling in many events. All her time in the gym training paid off as she achieved two personal best weights in the squat and the deadlift. She received state and national attention as she received four gold medals in the following lifts: bench press, squat, deadlift and combination. Brianne is one dedicated athlete and never misses an opportunity to train. She is a great person and definitely leads by example.
James Ross is an athlete who has an outstanding personality; he always finds the good in any situation. One of James's main assets is that he has the ability to get the most out of those around him by being himself and showing his wonderful positive attitude. He does this by being a team player. James currently participates in basketball, bowling, bocce, softball and track and field. He is known in the community as a go-getter and will volunteer for the local fundraisers helping his teammates. James will offer to help load a truck, move tables and chairs and lend a helping hand to anyone. He has the knack for seeing what needs to be done and doing it. He also has a knack of reading people and knowing when they need a pat on the back, an encouraging word, a smile or just someone to listen. James likes to spend time watching sports with his fellow teammates; he is an enthusiast when it comes to sports. James is a positive role model to his teammates and other athletes and a great advocate for Special Olympics. He is constantly recruiting his friends to play on a team or recruiting people to sponsor or volunteer for Special Olympics. He just recently took the Global Messenger training so that he can tell his story of what Special Olympics means to him. He currently serves as a team captain and an assistant coach to his team due to an injury that keeps him from playing basketball and softball. James never misses a meeting or an event and is there to cheer on his teammates and other athletes.
(Jefferson City) Missouri State Parks, a division of the Missouri Department of Natural Resources, was recognized for its outstanding contributions to Special Olympics Missouri at the 2015 SOMO Annual Awards Luncheon Jan. 17 in Branson. Each year, Special Olympics Missouri salutes those who have made significant contributions to the Special Olympics movement. Past honorees have included Missouri Association of Student Councils, Missouri Sports Hall of Fame, Whiteman Air Force Base, Missouri/Illinois Dodge Dealers and more.
You might have heard of a little event we like to call the Polar Plunge (SOMO.org/Plunge). Three of our Plunges are at state parks in Kirksville, Cape Girardeau and Lake of the Ozarks. Without the support of the Missouri State Parks, these plunges would not be possible.
Not only do they provide the lakes to make this event possible, but they manage parking needs, staff serve on the planning committees, provide tents, the Show Mobile, rangers, people to set up and tear down the event and the marina at the Lake of the Ozarks to house our Super Plungers on their 24-hour plunging adventure.
This year will mark the 20th anniversary of the Lake of the Ozarks Polar Plunge and Missouri State Parks/DNR have been involved every year from the beginning! In the last 19 years, the Lake of the Ozarks event has raised $2.2 million! The Kirksville event started in 2006 and has raised $204,000 and the Cape Girardeau event began in 2007 and has raised $328,000. The overall impact these three events have had in the last 20 years is $2.7 million!
Col. John Hoover from the Missouri State Parks Rangers was in attendance to accept the award.
Through this partnership, the Governor's 100 Missouri Miles was born in 2013 and kicked off at the Special Olympics State Summer Games which challenged our athletes to take part. Team Missouri used the Governor's 100 Missouri Miles as a training tool for the 2014 USA Games with one athlete recording more than 1,000 miles.
In 2014, when we came to them with the idea to hold a 100-plus mile multi-day cycling event they did not hesitate to help with the planning and execution of our first Cycle for the Future.
The Missouri State Parks staff goes above and beyond to support Special Olympics and our athletes.
It is an honor to recognize the Missouri State Parks – a division of the Missouri Department of Natural Resources as our Award of Excellence winner for 2014.
Finding the so-called perfect group of volunteers is not easy. It's important that they are dedicated, easy-going, hard-working and most important, dependable. For SOMO Central Area Program Director Diane Brimer, however, that perfect group fell in her lap a few years ago.
Between the different sports teams at Westminster, Brimer said she has enough volunteers to cover six different sessions of her Area Bowling Tournament in Fulton every fall.
While the idea was passed down from the SAAC at Westminster, the respective coaches of the teams involved have embraced the volunteering philosophy.
Hughes said volunteerism is something he tries to instill in all of his players as being an integral part of being not just on the team, but as a functioning member of society as well.
"We are more than happy to come out," Hughes said. "We wanted to do community service within our baseball program any way and this gave us a great opportunity to do so.
After a few years of being involved in this process, the student-athletes view volunteering as just one more thing that is expected of them as is going to class and practice. They revel in that opportunity to get out and give back.
"One thing I think it just helps community wealth; it builds it up," said Ryan Loethen, a junior baseball player at Westminster.
Another aspect for the athletes isn't so much on the personal level, but what it does for the team as well.
Even though most Special Olympics Missouri events are only one or two days a year, Loethen and Hughes said the impact for them can last much longer.
Brimer realizes how lucky she is to have the Westminster student-athletes and isn't planning on letting them stop volunteering anytime soon.


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Seaham Pottery 'Sailor's Return'
In my previous post I showed a grainy picture of a plaque titled 'Paul Jones' from an old auction catalogue. Below left is its pair, also inscribed 'A Present for E Towning'. Recently a similar plaque came up on eBay, this time with a hand-painted dedication to 'Harriet' – perhaps a sailor bought it as a gift for his sweetheart. If you were the buyer, please get in touch – I'd love a better photo.
This Sailor's Return transfer is attributed to Seaham, and also appears on very large frog mugs like the one below (click on the images to enlarge). The lustre decoration is similar to that on the 1847 Seaham jug (see centre below) in the Sunderland Museum. The final image shows the mug's immense size, next to a slop bowl with a similar transfer.
Closer inspection shows that the transfer on the slop bowl, although based on the same image, has some marked differences to the transfer on the mug. Note the lantern in the background in the right image, and the positioning of the sailor's bag.
The transfers on the slop bowl match those on a large frog mug in the Sunderland Museum, also attributed to Seaham Pottery. However, their lustre decoration is unlike any of the items attributed to Seaham so far.
To my mind, the items above look much more like wares from the Stockton Pottery of Thomas Ainsworth (see below). The circular plaques are sometimes found with impressed marks with a castle and anchor (see below centre), so the attribution for these items is pretty solid. Stockton items are often typified by what Ian Sharp has called 'slug trail' decoration. Note also the bands of lustre around the top of the jug, and compare them with the bowl and the mug above.
So the jury is out on the attribution of the slop bowl and second mug. As I've said before, attributing items on the basis of lustre decoration is an imprecise science, so more work needs to be done. As always, if you have items with these transfers that might help, please get in touch.
Seaham Pottery 'Paul Jones' transfer
Depending on your perspective, John Paul Jones was either the father of the US Navy and hero of the American Revolutionary War (below right), or a pirate (below left). The transfer that appears on Seaham Pottery items is untitled, so it is hard to be 100% sure of its subject, and I haven't been able to identify the source. The Sunderland Museum display states that the figure is Byron. But I've also seen him listed (incorrectly) as Jack Crawford. Perhaps there was a commercial advantage in deliberately leaving his identity open. However, I'm still haunted by the centre image below from an old auction catalogue. (If you own the plaque, please get in touch!) Someone at the Seaham Pottery decided, for one day at least, that the image was to be titled 'Paul Jones'.
The jug below, in the Sunderland Museum collection, is catalogued as 'probably Seaham Pottery', but I'm unsure on what basis. In fact, there are a whole group of distinctive transfers catalogued this way, which appear to be unique to the pottery. (More on that in the future.)
The jug has a bridge transfer (left below) very similar to the one on the large Seaham jug in the museum (centre below), dated 1847, but they are different - from memory, the transfer on the large jug is smaller. The below right image is from an Albion Pottery (Tyneside) plaque, c1864. Again it is different - note the triangular sail on the front of the large ship in the foreground.
The bowl below, from John Howard's archived items, has 'Paul Jones', accompanied by a very distinctive ship transfer, which to date I've only seen on the large Seaham jug in the museum collection (bottom centre and right). I'm unsure whether the two ships come from the same copper plate (without a high resolution image, it's difficult to tell). John's bowl has a bridge transfer that matches the one on the smaller jug above.
Anyway, here's the exciting thing, for me at least. I've long known that the plaque on the left below was likely Seaham, but I couldn't reconcile that with the fact that identical plaques are found with religious verses – same unusual large size (215 x 238 mm), buttery lustre, and distinctive decoration. Those verse transfers (the very same ones) also appear on 1850s' plaques with the John Carr & Sons impress. So how did the copper transfer plates find their way from south of Sunderland, up to North Shields on the Tyne?
I think the answer could be in the advert below. John Hedley Walker, the owner of the Seaham Pottery, moved his operations to Carr's Hill Pottery near Gateshead in 1849. Now, lest we get too excited, 'Carr's Hill' refers to a village, and not to the North Shields potter of the same name. However, this does provide a plausible explanation for why the copper plates might have migrated northwards beyond Sunderland to Tyneside.
Thanks to Ian Sharp for getting in touch and pointing out the Byron connection to Seaham. Byron married Annabella Milbanke at Seaham Hall in 1815. So the pottery had more reason than most to celebrate the poet. Byron spent time in Greece and had himself painted in Greek dress (see right below). So it's possible the transfer depicts him. We may never know until we discover the source for the transfer.
Seaham Pottery 'Mariner's Compass' transfer
The Seaham jug in the Sunderland Museum (see previous post) provides a catalogue of transfers used by Walker & Co at that pottery in 1847. This is a great resource, because Seaham items are nearly always unmarked, and difficult to attribute.
I thought I'd check items in my own collection and see if I could find a match, and where better to start than the Mariner's Compass, which has always been a favourite transfer.
The transfer on the Seaham jug is easily distinguishable from the transfer found on plaques attributed to Newbottle and Moore's (a Newbottle plaque shown on the right below). Compare the two details, each from the item above. N.B. the transfer on the jug (left) is applied to a curved surface, so there are distortions of perspective, but even so, it's clear that the transfers come from different copper plates. The shading of the fur on the lion's cheek is running in a different direction. The tufts of grass in the foreground are different.
But I did manage to find two items that matched (see below). Again, you need to remember the transfers are on curved surfaces, and stretched in different directions. But the shading on the lion and the tufts of grass now match. We know that the first jug, with coloured enamels was made earlier, because the other two objects show scratches on the copper plate that don't appear on the first.
Look at the lower right quadrant of the shield (third row below). The centre jug and the bowl have a dark line through the diagonal white band that doesn't appear on the first. Also look at the lower left points of the star in the centre of the compass (bottom row). The centre jug and the bowl have a scratch coming off the point at SSW, but again the first doesn't.
The scratches show that the Seaham jug in the Sunderland Museum and my bowl have transfers that are indisputably from the same copper plate. But are there any imperfections that tie together the two jugs? I believe there are. Firstly, the engraver looks to have slipped, and there's a small diagonal fleck that appears to the left of SSW. Secondly, the shading of the waistcoat of the male figure spills over slightly onto his left lapel.
So I have at least two items of Seaham Pottery. The bowl, which has very similar decoration to the Sunderland Museum jug, was likely also made by Walker & Co, c1847. The jug with coloured enamels is earlier but whether made by Walker & Co, John Allason, or an earlier partnership at Seaham, it's hard to say.
By looking at the other transfers on my jug and bowl we can further expand the Seaham 'catalogue'. If you have a similar item you'd like me to take a look at, please get in touch.
Seaham Pottery
Arguably, the most impressive item in the Sunderland Museum and Winter Gardens' collection of local pottery is the Seaham Jug. It was purchased with Art Fund money in 1994, a decade after Baker's book on Sunderland Pottery was last updated. The book contains surprisingly little information about the pottery that produced this spectacular item. Baker lists Seaham's products as 'brown ware and transfer-printed tableware'.
The inscription on the jug includes a painted factory mark 'Walker & Co, Seaham Pottery 1847' (click images to enlarge). However, his ownership is only hinted at in Baker.
I am indebted to local historian Fred Cooper for the press cuttings that follow, and to Shauna Gregg at the Sunderland Museum and Winter Gardens for the photos of pottery.
Baker suggests the pottery was built 'in 1836 by Captain Plowright of Lynn for the manufacture of brown ware'. The pottery was then owned by 'a group of workers from Dawson's Pottery who converted the works to the production of printed white ware', and operated between 1838 and 1841.
The first cutting is from the Newcastle Courant, November 6th, 1840. It advertises the sale of an earthenware manufactory with 'peculiar recommendations' to 'small capitalists especially'.
The list of effects confirms Baker's description above and includes 'four printing presses' and 'several copper plates, of new and approved patterns'.
Contrary to Baker's dates, the factory appears to have then been run by John Allason between 1841 and 1846. Here's an item with the impressed mark 'John Allason, Seaham Pottery' from the Sunderland Museum collection.
The advert below, interestingly from the Staffordshire Advertiser, tries to attract buyers from the other side of the country. The advert draws attention to the close proximity to the docks, railway and colliery district – factors which created a huge competitive advantage for north-east pottery manufacturers.
From 1846 to 1850, the pottery was owned by John Hedley Walker. So the Sunderland Museum jug fits in here, made in 1847 by 'Walker & Co'.
In March 1849, there's a notice in the Newcastle Guardian offering the premises to let 'immediately', and Walker appears to have moved to Carr's Hill Pottery, near Gateshead.
Walker sold the premises at an auction on December 11th, 1850 (this notice taken from the Newcastle Journal, November 23rd, 1850). The property is again offered for 'immediate possession'.
The property appears to have been bought by R.C. Wilson, but his ownership was short lived. An advert in the Newcastle Courant, September 10th, 1852, describes R.C. Wilson as 'a bankrupt', and offers the pottery's effects, including copper plates, for sale without reserve.
The misfortunes of the pottery continue with a fire in 1854 'entirely consuming' the premises (this cutting from the Newcastle Guardian, April 1st, 1854).
Interestingly, Baker writes that 'Fordyce in his History of the County of Durham, 1857, states that Seaham Harbour Pottery belongs to Mr John Hedley Walker'. However, it seems unlikely that after having to let the property in 1849, and after it had driven R.C. Wilson to bankruptcy, that Walker would want to rebuild it.

redpajama_set_name: RedPajamaCommonCrawl


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Water wasn't very hot for a mid afternoon bath. Curtain material could be higher quality for a boutique hotel. Several piles of dog or cat faeces on the roof terrace. Water splashed out of sink due to the modern but impractical shape of the basin.
The mirror was across the room from the only plug to use the hairdryer. Had to dry my hair without using a mirror.
Everything was perfect, the staff are excellent and the hotel stunning.
Excellent place to stay right in the middle of the old town. Highly recommended.
This hotel was AMAZING! My boyfriend and I spent our last night in Crete here and it more than exceeded our expectations. It's located in the middle of old town Rethymno steps away from a ton of shops and restaurants/cafes/tavernas. If you're into antiques, there is a shop next door that is literally the best antique store I've ever stepped foot in. From the moment we arrived to the moment we left, our time at the hotel Bellagio was incredible. We were welcomed by Catherine, the host of the Bellagio. She greeted us with coffee, water, and pastries while waited for her to get our keys. She sat down with us and showed us a map where we could find nearby parking, great restaurants, and other local sights to see. They had a great breakfast buffet. Our room was gorgeous. We had a private balcony with lounge chairs and a table. The room was quite spacious and had its own steam room which we used twice while we were there. Catherine was THE BEST. Definitely recommend this hotel. Will be staying here next time we visit Crete.
The staff were very personable, friendly and very helpful. Good people always make the difference.
Our room had been freshly painted so there was still a strong smell of paint.
Excellent location and the reception staff was extremely helpful and friendly. We would definitely stay there again.
Very helpful and friendly staff, great location.
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Are the Saudis looking for a new partner in the Middle East?
Ah, a smart diplomatic move. Engage the supporters of Syria and give them incentives not to support the regime. Why, it sounds like a well-considered attempt at foreign policy that any competent State Department might have tried.
What has the US administration done to bring Russia along?
The State Department on Monday postponed a meeting scheduled for Wednesday in The Hague between senior diplomats from the United States and Russia due to "ongoing consultations" over the chemical weapons attack in Syria.
The meeting was to have discussed plans for an international peace conference to end the civil war in Syria.
Right. I forgot. I guess its time to "reset" again.
Look, it is clear from these sorts of reports that the US is considered to be weak and undependable by many in the region. Right now, the move toward attacking Syria has to be considered more of an emotional and political response than one that will in any way help solve the situation there. While one may not be a huge fan of the Saudis, their overtures to Russia are certainly something that should signal the US that it has a pretty basic problem on their hands. It's an indicator, and not an indicator that reflects very well on this country. Most importantly it points out how dismally poor our foreign policy is and has been in the region. It is so poor that an important ally openly courts Russia and fears absolutely no backlash from us. Additionally, they are engaged in the sort of diplomacy a world leader like the US should have been engaging in with Russia all along. Instead we have an indecisive President drawing and re-drawing red lines while his State Department cancels meetings with an important player in the region.


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Email: john.kalled@kalledlaw.com
John Pierce Kalled is the Managing Member of the Kalled Law Offices, PLLC, with offices in Ossipee, New Hampshire. Mr. Kalled attended college at the University of New Hampshire, receiving a B.A. Degree in 1977, magna cum laude, and was invited to become a member of Phi Beta Kappa. He received his J.D. from Albany Law School at Union University, Albany, New York in 1981.
From 1979 to 1981 Mr. Kalled was a law clerk at the Law Offices of Charles E. Snow, PC, in Albany, New York, a firm concentrating in large scale construction litigation and surety claims consulting. From 1982 to 1992 Mr. Kalled served as an associate in his family's law firm concentrating in medical malpractice, products liability, contract and family litigation. He became the proprietor of that firm in 1992. From 1992 to the present, Mr. Kalled's practice has focused upon aviation, business, contract, family and litigation matters.
Mr. Kalled has served as a member of the Board of Governors of the New Hampshire Trial Lawyers Association from 1997 to 2002 (Amicus Committee, 1998 to 2005), as the Chairman of the General Aviation Subcommittee, Aviation Litigation Section, of the American Bar Association from 1997 to 2000, and as a Panel Attorney with the Aircraft Owners & Pilots Association from 1991 to the present. He is an active member of the American Bar Association, the National Transportation Safety Board Bar Association, and the Family Law and Business Law sections of the New Hampshire Bar Association.
Mr. Kalled is a member of the bars of the United States Supreme Court, 1993, United States Court of Appeals, First (1985), Federal (1990), & DC (1998) Circuits, United States Court of Federal Claims, 1990, United States District Court, District of New Hampshire, 1982, New Hampshire Bar, 1982 and the District of Columbia Bar, 1998.
Mr. Kalled is the recipient of the President's Award, New Hampshire Bar Association for distinguished service on the New Hampshire Bar Association Special Committee on Rules of Civil Procedure, and is honorably rated by Martindale-Hubbell. Mr. Kalled served honorably with the 363rd Combat Group, 9th Air Force, USAF, from 1972 to 1976.
Aircraft Transactions & Litigation; Business Law; Family Law; Civil & Criminal Trial Practice
Admitted:
1982, New Hampshire and U.S. District Court, District of New Hampshire
1985, U.S. Court of Appeals, First Circuit
1990, U.S. Court of Federal Claims
1993, U.S. Supreme Court; 1998, District of Columbia and U.S. Court of Appeals, D.C. & Federal Circuits
Albany Law School, J.D.
University of New Hampshire, B.A., magna cum laude, 1977
New Hampshire Bar Association; District of Columbia Bar.
Phi Beta Kappa. Commercial Pilot, Multi/Single-Engine Land & Instrument Ratings, United States.
Born: Wolfeboro, New Hampshire
U.S. Air Force, 363rd Combat Group, 9th Air Force, 1972-1976