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The zstm are astm e1527 for information and are not part astm e1527 the procedures prescribed in either this practice or Practice E This document cannot replace education or experience and should be used in conjunction with professional judgment.
Section 1 is the Scope. Both are environmental site assessments for commercial real estate. Section 13 astm e1527 additional information regarding non-scope considerations see 1. One of the purposes of this practice is to identify a balance between the competing goals of limiting the costs and time demands inherent in performing an environmental site assessment and the reduction astj uncertainty about unknown conditions resulting from additional information.
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Work Item s – proposed revisions of this standard. Section 1 is the Scope. Additions and deletions in the terminology sections, including the addition of such terms as business environmental risk, activity and use limitations, engineering controls, institutional controls, historical recognized environmental condition, and material threat. While use of this practice is intended to constitute all appropriate inquiries for purposes of the LLPsit astm e1527 not intended that its use be limited to that purpose.
Astm e1527, an evaluation of business environmental risk associated with a parcel of commercial real estate may necessitate investigation beyond that identified in this practice see Sections 1.
Link to Active This e11527 will always route to the current Active version of the standard. Users are cautioned that federal, state, and local laws may impose environmental assessment obligations that are beyond the scope of this practice. Controlled substances are not astm e1527 within the scope of this standard. Users are cautioned that federal, state, and local laws may impose environmental assessment obligations that astm e1527 beyond the scope of this practice.
Work Item s astm e1527 proposed revisions of this standard. Sections 7 r1527 12 are the main body of the Phase Astm e1527 Environmental Site Assessmentincluding evaluation and report preparation. The term recognized environmental conditions means astm e1527 presence or likely presence of any hazardous substances or petroleum products in, on, or at a property: There is a point at which the cost of information obtained or the time required to gather it outweighs the usefulness of the information and, in fact, may be a material detriment to the orderly completion of transactions.
Section 3Terminology, contains definitions of terms not unique to this practice, descriptions of astm e1527 unique to this practice, and acronyms.
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text:
-14.2 C
Home Community City wants to spend over $230,000 even though plaza is not yet...
City wants to spend over $230,000 even though plaza is not yet approved
Dan Gray
In a move which appears to be placing the cart before the horse, city council is going to be asked Monday to allow PUC to start doing electrical work for the downtown plaza.
According to a report to council from Tom Vair, deputy CAO, the more than $230,000 ask is already in the approved budget for the project. However, the full project is not fully funded nor approved yet.
Earlier this week the city started a fundraising campaign which has faced severe backlash on social media, to raise the $2.5 million deficit in the project's budget.
"The completion of the electrical servicing work is an important component of the
downtown plaza project. If the project is to proceed in any form, the electrical
servicing work will need to be undertaken to facilitate the project," says the report.
Why is the city in such a hurry to approve this, when the overall project is both not well supported by the public and not yet fully approved?
Is this a way to come back in a month or so and say… "well, the electrical is already started, so we might as well finish the job," if the money isn't fundraised?
Mayor Provenzano is on the record as saying even if the fundraising doesn't happen, he expects there to be a plaza of some sort built.
So far, multiple calls on the city's Facebook and other social media platforms to re-direct this money into other uses have gone unanswered.
SaultOnline will be airing Monday's council meeting which starts at 4:30 on our Facebook page and ONNTV. We will also bring you the results, live, of those councillors who vote to approve this expenditure as well as those who vote against.
Downtown Plaza
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A native of Centre Wellington, Ontario, I have worked as a freelance journalist all over the province of Ontario. Most recently I ran a local news page in my hometown of Fergus, Ontario. I look forward to bringing my experience covering breaking news, municipal matters and general interest stories to the team at Superior Media and communities we serve.
Your Local Weather Today – Friday, January 27
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Cathy September 12, 2021 @ 5:14 pm At 5:14 pm
This will not make me want to go downtown. I do not even shop downtown except for the mall. It will be full of people who live down there and not those who do not. Downtown area scares me and I am sure I am not the only one.
KR September 12, 2021 @ 9:42 am At 9:42 am
If there weren't so many sycophants around him the out of touch with reality mayor would be gone by now.
RON Schinners September 12, 2021 @ 7:46 am At 7:46 am
What part of ,"We don't want this waste of taxpayers money " do the Mayor and council don't get?
Is there not one ounce of common sense with these individuals.
Bill September 11, 2021 @ 10:35 pm At 10:35 pm
What the people who built our city want doesn't not mean a thing. The politicians build it for nobody but themselves. We need people to run our city that know what the value of a dollar is, not greedy crooks.
Joe September 11, 2021 @ 7:54 pm At 7:54 pm
It's happening..give it a rest.
KR September 11, 2021 @ 6:38 pm At 6:38 pm
Would someone please oust the out of control rogue mayor!
Remi September 11, 2021 @ 6:07 pm At 6:07 pm
The Mayor Provenzano is already fixing up the boardwalk for tourist. The problems with that is tourists don't stay there long. When the tourists show up the bums and drug addicts come out of the woodwork and beg for money , that's when the tourists leave. Building a new $ 230,000.00 plaza will only move the drug addicts from the boardwalk to the New Plaza , smart thinking Mr. Provenzano.
"Building a new $ 230,000.00 plaza"
The last figure I heard was about $12,000,000.00
Charles September 11, 2021 @ 5:16 pm At 5:16 pm
Mayor and council have become nothing more than servants for the downtown crew.
ram guy September 11, 2021 @ 4:59 pm At 4:59 pm
The sign will read…Provenzano Plaza.
SafeCity September 11, 2021 @ 2:59 pm At 2:59 pm
So who cares what the taxpayers have to say is basically what he says. And the rest of you councillors have zero backbone to stand up for the constituents.
This project has not been given the go ahead, but it will and it will cost us a lot.
This is another fiasco like the Studio 10 property purchase, cutting daycare and bussing. And let's not forget how this mayor and his council bought into the whole line of BS that the former Fire Chief fed them and cut that service. That also cost us a lot. We have had over 6 years of this mayor's amateur or immature moves, not sure which description fits the best here. Maybe both. Next thing you know he'll be trying to slide into some high paid city job and we'll be stuck with him longer.
Moi September 11, 2021 @ 2:39 pm At 2:39 pm
We didn't want the new city logo….so what.
We didn't want to buy Studio 10 property….so what.
We don't want the ferrochrome plant….so what.
We don't want the downtown plaza….so what.
Council do what they want, when they want, while we have to beg for local improvements. The roads are a mess. Crime is ever increasing and criminals are set free to commit more crimes. The drugs are freely flowing and many are dying.
But everything will be better if we spend millions on a downtown plaza and please the downtown merchants. What a crock of horse manure!
Joe September 12, 2021 @ 2:29 am At 2:29 am
Everyone hates crime but everyone keeps voting Liberal.
BBB September 11, 2021 @ 12:18 pm At 12:18 pm
It is way too late for council to get out of this one. To save face they will go ahead with any possible support coming for the merchants in the area of the build. It is just astounding that they want to put a rink in there when we have one at clergue park and let's not forget the Bondar tent could handle almost all of their other intentions.
Jan September 11, 2021 @ 8:33 am At 8:33 am
What council fails to see is that the public do not want this overpriced plaza. I am just wondering how many contracts council has signed already.
Kris September 11, 2021 @ 10:33 am At 10:33 am
Exactly!!
This money can go to better things like hiring more police officers, the opioid crisis, the crime rate!!
Not too mention the waste of money of a train station when we don't even have passenger service! A 6 million dollar train station for an over priced tourist train that is always half empty and only runs 3 months of the year?!
Bring back passenger service from sudbury right up to white river. We don't need a full train, just a bus car with a baggage car like they have from Sudbury to White River!! It will always be full.
We need a new city council. One that actually cares for what is important in this city.

redpajama_set_name: RedPajamaCommonCrawl


SKU: 554724-048. Category: New Air Jordan 1 (I). Tag: Air Jordan 1 Mid Royal Splatter.
This Air Jordan 1 features a Black and Blue leather upper, perforated toes, paint speckles and Orange branding on the tongues and insoles.


18 Spring Decor Ideas . Spring is finally here, and bloggers everywhere are sharing their spring home tours with us. You'll find some great inspiration for your home, and be sure to click on your favorites to see the rest of their ideas!
18 Spring Decor Ideas Ideas!. These decorating ideas for spring are perfect for every home no matter what your style is. There's an idea here that will likely be perfect for you!


Play Lotto Games and Live Your Dreams!
Lotto is a synonym for the word dream. I am not talking about the dreams that we have when we go to sleep at night. I am talking about daydream. Lottery games are the fuel for those daydreams – quite frankly, I don't think that anybody has a daydream in which they are not rich.
When we daydream about what our lives would be like if we won the lotto, the dreams will be about endless possibilities rather than everyday problems. We do not daydream about our work or home problems we daydream about a better life, far from where we are now. However, there are lottery winners who have allowed fame and fortune to be their downfall. Whatever the case may be, daydreaming about a brighter future is very beneficial.
Our stress levels can be reduced by simply having optimistic thoughts about the future. You may feel that you are overworked as well as underpaid. Instead of thinking about how unfair it is – look forward to the day that you win the jackpot. On that day you can get up and go, leaving the situation for good. Concentrate on that thought. Also, if you are a stay at home mommy – like me – in the school holidays with a bunch of noisy kids to take care of, looking forward to the day when you can laze on a pure white beach, somewhere far, far away in a tropical paradise with a cocktail in your hand can make the current situation seem a little less hectic.
Lastly, to daydream about being a jackpot winner often makes us aware that we want to do something to benefit society as well as ourselves. Like those unconscious personal ambitions mentioned a moment ago, when we realize that we would really like to help a certain charity or community, we can begin doing so, albeit in a smaller way than we would probably be able to after a life-changing win.
We all strive towards something – focus on what and where you want to be in 10 years from now. With that said – keep playing the lotto, otherwise your daydreams are nothing more than just that.
This entry was posted in Euromillions, HUGE Lottos, OZ Lotto, Superena Lotto, UK Lottery, USA Lotteries, USA Megamillions, USA Powerball and tagged Dreams, Lotto. Bookmark the permalink.


In early November of 1965, at the height of the cold war, 30 million people living in the most densely populated region of the United States experienced a cascading power failure which blacked out almost the entire Northeast in less than fifteen minutes. Rising to the occasion, New Yorkers assisted each other in a spirit of cooperation and community uncharacteristic of ordinary city life. Twelve years later, in the summer of 1977, the New York metropolitan region experienced another massive power outage, but this time the popular response was quite different. Devastating riots and looting engulfed the poorer sections of the city, inflicting enormous economic damage at a time when New York City was already on its knees.
This comprehensive site focuses on the history of the 1965 blackout in the Northeastern United States and the 1977 blackout in New York City. These two landmark events in the history of technology and the cultural history of America are recounted in a number of ways: through interviews, excerpts from various media, a timeline of events, recent historical writing, and, most compellingly, a growing database of first-hand recollections entered by visitors to the site. These recollections cover both the behind-the-scenes experiences of those who worked for the utility companies and the people who lived through the events. The site is therefore an excellent example of how to create an oral history archive on the Web, as well as a good source for understanding the tremendous social, cultural and technological impact the blackouts had on the people who lived through them.
This site, produced by the American Institute of Chemical Engineers, looks at the history of cans and bottles used for soft drinks and other beverages. In particular, it looks at the rise of the plastic container, and includes a biography of DuPont inventor, Nathaniel Wyeth, who first developed a usable polymer bottle. A timeline covers the history of beverage containers and innovations in the field from the late nineteenth century to the present, noting major milestones. The POP site also looks to expand this history, recording the memories and views of those who have worked in beverage container research and development. Discussions cover a variety of topics, including reasons why beer bottlers have remained resistant to plastic containers.
Welcome to the history of electric vehicles site, the first online archive created to encourage electric vehicle enthusiasts to help preserve the recent history of electric vehicles. This project offers a unique opportunity for electric car owners, drivers, and enthusiasts to chronicle their own history by adding to an online archive. Drivers and owners of electric vehicles can contribute to the growing online archive, and people interested in the history of technology can use this site to learn about the history of an emerging technology. Sponsored by the Alfred P. Sloan Foundation, this site is a non-profit historical research project intended solely for non-commercial use.
This site examines the history of vehicles (mostly automobiles) powered by electricity rather than by an internal combustion engine. Providing an overview of the technology and its development, this site includes several recent articles on electric vehicles as well as important historical pieces. Most significantly, the site encourages current and former owners and drivers of electric vehicles to contribute their recollections to a lasting archive. That growing archive contains stories from the early days of electric-powered vehicles from engineers, early adopters and others involved with their production and use. The site is useful for understanding the goals, attitudes, successes and failures of current and former electric vehicle advocates.
The Henry Ford is widely recognized as one of the country's premier historical attractions and has been cited as having "the finest collection assembled documenting the American experience." Each day, thousands of children and adults from down the street and around the world are inspired by their experiences at this wonderful place.
This site is dedicated to exhibiting the treasures of the Ford Museum and Greenfield Village that include a replica of Thomas Edison's Menlo Park complex. The site is rich in details and images relating to the history of invention in America. Online exhibits include Buckminster Fuller's "Dymaxion House," the 1811 Dickson Steam Engine, the Showroom of Automotive History, biographies of famous inventors, and the first Kodak Camera. These exhibits provide a useful introduction to various inventions. The site also provides information about the archives and collections contained in the research center, most of the materials have not yet been digitized. Collections include: the Ford Motor Company archives, the Edison collection and Edison Institute archives, and the archives of the Boston and Sandwich Glass Company, Stickley Brothers, the D.S. Morgan Company, and the Gebelein Silver Company. Teachers and researchers will find the site useful.
Providing support to teachers at all levels who are attempting to teach the history of computing.
Site, which has not been updated since 2002, contains some images of historical computing devices and lectures from Dr. Tim Bergin's History of Computing class at American University. A related bibliography is also made available.
The books in this collection bear nineteenth century American imprints, dating mainly from between 1850 and 1880. They have been digitized by the University of Michigan as part of the Making of America project, a major collaborative endeavor to preserve and provide access to historical texts. Currently, approximately 1,500 books are included. The collection is particularly strong in poetry and in the subject areas of education, psychology, American history, sociology, religion, and science and technology.
The collection is divided into seven general themes: Civil War, Slavery and Abolition, Religion, Education, Self-Help and Self-Improvement, Travel and Westward Expansion, and Poetry. Each section opens with a 200-word descriptive essay, and each book featured on the site is accompanied by notes on the author, full title of the work, date and place of publication, and the publisher. The site is keyword searchable and can be browsed by subject, author, and title. The site is ideal for exploring late-19th-century literature and popular culture.
to develop this land of Diaspora that we have built.


Please Support Our Global Campaign for Warning Labels on Paper Cups!
Recently, we launched a campaign to lobby the Federal Government to require warning labels on paper cups. Please join our FACEBOOK application. When you allow access to this application, you are simply helping to spread the word to your friends. You can also show support by signing our petition!
We want people to know that, not only are paper cups NOT made from recycled material, but they're also NOT RECYCLED because of the polyethylene coating on them. In addition, paper cups give off methane gas in landfill, a greenhouse gas with 23 times the heat-trapping power of carbon dioxide. In North America, 20 BILLION paper cups are used annually. We really do need warning labels on paper cups! Please support this campaign by sending an email to a friend, someone in the media and/or your Federal Environment Minister (In Canada, Peter Kent) - see links above. We also have a Facebook recommend button above to help spread the word to your friends. It only takes a second to click it!


Obesity is a one of the greatest health issues facing communities and health systems; the related disease states are numerous. Methods to promote weight loss are often of limited success in terms of sustained weight loss.
Food for Thought is a group based 8-week program with a psychological and non-dieting approach aimed at assisting participants to improve their eating behaviours and relationship with food. The experiential group-based clinical intervention utilises Allied Health professionals including dietitians, psychologists and exercise physiologists. Both qualitative and quantitative data were collected to conduct an impact evaluation with the aim of also conducting an outcome evaluation in the future.
participants who completed the qualitative impact survey agreed or strongly agreed that they benefited from their participation in the program across a range of impact measures.
The Food for Thought program was well received by participants and has demonstrated an improvement in eating behaviours and a reduction in weight at conclusion of the program. The results indicate the participants experienced a shift in thinking about food, eating behaviours and weight with the hope that these changes will be sustained and result in long-term improvements to both weight and health.


text:
\section{Introduction}

Many professionally created historical maps are now accessible online as images through library repositories or map archives. These maps provide a unique opportunity for scientific studies that require long-term, historical geographic data, which do not exist elsewhere~\cite{chiang2020using}. However, these map images represent only a small fraction of the available map collections. For example, searching map records using WorldCat\footnote{WorldCat is a catalog system that indexes library collections from more than 100 countries and 17,900 libraries.} shows that less than 4\% of all map records in the US are digitized and published online. In contrast, the remaining 96\% of the map records are paper maps. With the advances in low-cost, high-speed automated scanners, the main reason that prevents these paper maps from being published online is the tedious manual process to compile metadata for the maps once they are scanned as images. At the University of Southern California, cataloging a map sheet requires about 30 minutes of manual work by a professional library curator, which would require 30 years to process all 125,000 historical topographic maps from the USGS (United States Geological Survey). As a result, even after paper maps are scanned into images, these map images are still not searchable because they do not come with any metadata, such as the geographic coordinates of the map center, the map scale, and place-related information on the maps.  For making all historical maps accessible, the first and crucial step is to increase the level of automation in generating a rich set of metadata for these maps so that they can be indexed and searchable by location and keywords.

Existing approaches to automatically generate map metadata rely on optical character recognition (OCR) tools that are designed for conventional document images but not maps~\cite{Chiang2015-jd}. For example, OCR tools focus on recognizing individual words and paragraphs in a document~\cite{yu2016recognizing, chiang2014survey, chiang2020using}. They do not handle map text that can have varying orientations and spacings. For example, each word of the place name ``Double Head Mountain'' on a map can be far away from each other and can have varying text orientations following the geographic features on the map. Also, on a map, text regions that are close to each other are not necessarily a part of the same location phrase (e.g., a place name). In Figure~\ref{fig:link_eg}, the word \textit{Fall} is close to the word \textit{Burgettville}, but they are not a part of the same phrase. The word \textit{Fall} should be linked to the word \textit{River} to constitute the location phrase \textit{Fall River}. Therefore the distance between two words is not sufficient to predict the linkage between individual words. Figure ~\ref{fig:link_eg} also shows a map area that contains densely packed text labels with the same font style and font size, which makes automated processes for generating map metadata challenging.

This paper presents a complete approach and its implementation, mapKurator, to automatically generate a rich set of metadata from map images using their text content. mapKurator first detects individual words from a map using an OCR tool (e.g., ~\cite{liao2018textboxes++,visionapi}) and then employs a deep neural network to link individual words in the OCR results (e.g., ``Los'' and ``Angeles'') to complete location phrases (e.g., ``Los Angeles'') based on their text and visual information (Section~\ref{sec:linking}). Next, mapKurator uses a geocoding service to find candidate geolocations for each detected location phrases and then and identifies spatial clusters of the candidates to remove unlikely geolocations and generate approximate geolocation for the map (Section~\ref{sec:geocoding}). Geocoding is the process of comparing text strings of addresses or place names to a large set of georeferenced data to generate the geocoordinates of the text strings (e.g., ~\cite{ge2005address, rashidian2018easergeocoder}). Once the approximate geolocation of the map is identified, mapKurator queries the LinkedGeoData to match the location phrases to entities in the LinkedGeoData using their location and text similarity (Section~\ref{sec:entity_matching}). The result is a set of metadata that is linked to large external geospatial knowledge bases (i.e., LinkedGeoData). The linked metadata in the RDF (Resource Description Framework) format support complex queries for finding and indexing historical maps, such as retrieving all historical maps covering mountain peaks higher than 1,000 meters in California.

\begin{comment}
$L_{wce} = - (\alpha y log\hat{y} + \beta (1-y) log(1-\hat{y})) $

\begin{multline}
L_{f1} = 1 - F_1(y, \hat{y}) = 1 - \frac{2 Precision(y, \hat{y}) Recall(y, \hat{y})}{Precision(y, \hat{y}) + Recall(y, \hat{y})} \\
= 1 - \frac{1}{2} = \\
precision = \frac{||argmax(y) \cdot argmax(\hat{y}) ||}{||argmax(y)||} \\
recall = \frac{||argmax(y) \cdot argmax(\hat{y}) ||}{||argmax(\hat{y})||}
\end{multline}

\begin{multline}
precision = \frac{y \cdot \hat{y}}{sum(y)} \\
recall = \frac{y \cdot \hat{y}) ||}{sum(\hat{y})}
\end{multline}

$L = L_{wce} + L_{f1}$
\end{comment}

\section{Overall Approach to Generate Map Metadata}
The overall approach of mapKurator for generating map metadata includes three major modules. In the first module, mapKurator generates complete location phrases from the input map. In the second module, mapKurator geolocates the place phrases and determines an approximate geolocation of the map. In the last module, mapKurator matches the georeferenced place phrases to entities on the LinkedGeoData to generate linked metadata for the input map.

\begin{figure}[h]
\centering
\subfloat{\includegraphics[width=0.5\columnwidth,height=0.45\columnwidth]{images/eg1.png}}
\subfloat{\includegraphics[width=0.5\columnwidth,height=0.45\columnwidth]{images/dense_eg.png}}
\caption{Challenges for text linking. Left: Distance is not the only deterministic factor for linking separate text regions. Right: Some maps contain densely-packed text regions.}
\label{fig:link_eg}
\end{figure}

\subsection{Generate Location Phrases from Words}~\label{sec:linking}
The goal for this module is to construct full location phrases from single words by first discovering the words that are in the same location phrase (text linking) and then sorting the words within each location phrase (e.g., ``Los'' should be before ``Angeles''). We formulate this text linking process as a query-retrieval problem. First, mapKurator runs text detection and recognition tools, such as Google Vision API ~\cite{visionapi}, to extract separate words and their bounding boxes. Then, mapKurator takes a detected text region as the query word and returns the potentially linked text regions by examining all other text regions on the map using its textual predictor  and visual predictor . mapKurator repeats the querying process for every single text region on the map to construct a graph of the linked words. The \textbf{textual predictor }in mapKurator makes the first prediction to determine if a pair of words are linked together (i.e., in the same location phrase) using their textual information. Textual predictor  is a binary classification model that looks at all the individual text regions on the map given a query region and filters out the ones that are unlikely to be linked to the query region. The purpose of the textual predictor  is to effectively narrow down the search space for visual predictor. The \textbf{visual predictor }exploits the prediction from textual predictor  and utilizes image information to refine the prediction. Visual predictor relies on a semantic segmentation model to harness the visual context and gives pixel-level prediction on the linkage relationship. Finally, the \textbf{consensus module} in mapKurator combines the output from both predictors and makes the final decision. By combining the results from the two models, mapKurator could achieve a better F1 score compared with each separate model. mapKurator uses the final linkage decision to construct a graph and find its connected components. mapKurator sorts the elements in each connected component according to a simple heuristic to constitute each location phrase.

\begin{figure*}[t]
\begin{center}
\includegraphics[width=\linewidth]{images/outline.png}
\end{center}
   \caption{Generating location phrases from historical maps. Our method takes full map images as input, and runs text detection and recognition as a preprocessing step which yields recognized text content and predicted bounding boxes. Both textual predictor  and visual predictor  are working towards pairwise linkage prediction, whereas textual predictor  considers text-related information, and visual predictor  considers image-related information. Text-related information is the information that can be derived from the text region itself, such as location, orientation angle, word embedding, etc. While the \textbf{visual information} comes from the neighborhood of a specific text region on the image. After pairwise linkage prediction, we construct a graph, $G=\{N,E\}$, whose nodes, $N$, are separate text regions and edges, $E$, are predicted linkages. By computing the connected components and sorting their elements, we construct the location phrases. }
\label{fig:outline}
\end{figure*}

\subsubsection{Textual Predictor with Deep Metric Learning}

Given a pair of text regions such as ``Jefferson'' and ``Street'', we want the textual predictor  module to predict whether they belong to the same location phrase. If they do belong to the same phrase, which is true for this case, the classifier should output 1, otherwise 0. We call the pair ``Jefferson'' and ``Street'' a \textbf{positive pair}, and a pair like ``Jefferson'' and ``Western'' that do not belong to the same location phrase, a \textbf{negative pair}.

The textual predictor  in Figure ~\ref{fig:outline} takes pairs of text regions as input. The multi-modality embedding module in textual predictor  utilizes the textual information associated with the input text regions. We define the inputs for text region $B_i$ as following: text content $t_i$; center location $c_{xi}$, $c_{yi}$; angle $a_i$ which is the clock-wise bounding box orientation angle with 0 degrees corresponding to a vertical and 90 degrees corresponding to a horizontal orientation; average font area size $f_i = b_i * h_i / len(t_i)$ where $b_i$ and $h_i$ are the width and height of the bounding box; capitalization $p_i$ which equals to 1 if \textbf{all} the characters in the word are in upper case, otherwise 0. Both location and font size are normalized to [-1,1] using formula $2x/(x_{max} - x_{min}) - 1$, and the angle is normalized to [0,1]. We use the Word2Vec ~\cite{mikolov2013efficient} model to embed the text content $t_i$ as a 50 dimensional feature vector $w_i$. The Word2Vec model weights are initialized by the pre-trained weights on the Wikipedia 2014 + Gigaword 5th Edition corpora (6B tokens, 400K vocab) using the GloVe ~\cite{jeffreypennington2014glove} algorithm. Then we concatenate $\{w_i, c_{xi},c_{yi},a_i, f_i, p_i\}$ to obtain the textual feature representation $r_i$ of text region $B_i$. To determine if the two text regions should be connected together, we perform binary classification on the textual features. Also, we enforce the textual feature to be similar in embedding space if two regions are connected. We use binary cross-entropy as the classification loss:
\begin{equation}
    L_{CE} = -\frac{1}{N}\sum_{i=1}^{N}y_i log(p_i) + (1-y_i)log(1-p_i)
\end{equation}
\noindent where $y_i$ is the ground truth label for sample $i$, $p_i$ is the prediction for sample $i$, and $N$ is the number of samples. We also apply triplet loss ~\cite{taigman2014deepface, hoffer2015deep}on the embedding space to enforce similar features to be close to each other, and dissimilar features far away. The loss equation reads as:

\begin{equation}
    L_{tri} = \sum_{i}^{N}[||r_i^a - r_j^p||^2_2 - ||r_i^a - r_k^n||^2_2 + \alpha]_+
\end{equation}
where $r_i = concat(w_i, c_{xi},c_{yi},a_i, f_i, p_i)$ and $r^a$ is the anchor feature in triplet loss, $r^p$ is the positive feature, and $r^n$ is the negative feature.

\subsubsection{Visual Predictor with Weekly Supervised Segmentation}
The visual predictor  first draws a minimum bounding box that includes all the retrieved positive candidates from the textual predictor . Then it predicts the probability of a pixel belonging to a text region that should be connected to the query text region. The bounding box is expected to be relatively small compared with the full map image since the textual predictor  has removed the majority of the negative samples. Thus the previous step saves us from looking at the whole image so that we could focus on a smaller neighborhood of the query text region.

We use the U-Net structure ~\cite{ronneberger2015u} for our weakly supervised segmentation model. The model takes two inputs: (A) cropped RGB image around the neighborhood of the query text region (see the first column of Figure ~\ref{fig:unet-output}) and (B) a mask that highlights the query text region (see the second column of Figure ~\ref{fig:unet-output}). The two inputs have the same size, letting us concatenate the two inputs along the channel dimension to form a 4-channel tensor as the direct input for the model. The output of the model is a probability map that indicates the probability of a pixel belonging to a connected text region of the query region. From the probability map (shown in the last column in Figure ~\ref{fig:unet-output}, we use the threshold $p=0.5$ to binarize the prediction to produce the second input (see lower left image in Figure \ref{fig:combiner}) for consensus model.  We use \texttt{sigmoid} activation for the last layer of U-Net and binary cross-entropy for the loss.

\begin{figure}[h]
\captionsetup[subfloat]{farskip=2pt,captionskip=2pt}
\centering
\subfloat{\includegraphics[width=0.99\columnwidth]{images/unet1.png}} \\
\subfloat{\includegraphics[width=0.99\columnwidth]{images/unet2.png}} \\
\subfloat{\includegraphics[width=0.99\columnwidth]{images/unet3.png}} \\
\caption{Visualization of the input, ground-truth and output of weakly supervised segmentation module with U-Net structure. The first two columns are inputs, the third column is the ground-truth output, and the last column is the prediction of the model.}
\label{fig:unet-output}
\end{figure}

\subsubsection{Consensus Model}
We combine the results from textual predictor  and visual predictor  using a consensus module. Assume that the textual predictor  produces $M$ candidate text regions for the query region $B_q$, and each candidate text region is referred to as $B_i$ with $i\in[1,M]$. Assume that the minimum bounding box that contains candidate text regions is of size $P\times Q$, we pad the bounding box to a square whose edge length equals to $max(P,Q)$. The padding color is ($m_r$,$m_g$, $m_b$) where $m_r$, $m_g$, $m_b$ define the mean color density for each channel. To facilitate batch training, we resize the padded image of size $max(P,Q)\times max(P,Q)$ to size $N \times N$ with either upsampling or downsampling and feed into the visual predictor . Thus the output for visual predictor  $S$ is a tensor of size $1\times N\times N$ where $N$ = 256 in our experiments. We use the following metric to determine if $B_i$ is a consensus for both modules.
\begin{equation}
    \frac{1}{||B_i||}(\sum_j^N\sum_k^N(B_i \otimes S_{jk})) > \theta
\end{equation}
where $\otimes$ is the element-wise multiplication, and $\theta$ is a hyper-parameter that serves as a threshold. This metric checks if the average probability of the overlapping region from textual predictor and visual predictor is higher than a certain threshold. We use $\theta = 0.5$ as the threshold in the experiments.

\begin{figure}[t]
\begin{center}
\includegraphics[width=\linewidth]{images/combiner.png}
\end{center}
   \caption{Consensus module to find the common retrievals of visual predictor  and textual predictor  }
\label{fig:combiner}
\end{figure}

\subsubsection{From pairwise linkage prediction to location phrases} \label{sec:pair2final}

After we obtain pairwise predictions, we construct a directed graph $G = \{N,E\}$ where nodes $N$ are the separate text regions from the map, edges $E$ are the predicted linkages from the previous step. If the region $r_j$ is classified as a linked region with query region $r_i$, we draw a link from node $i$ to node $j$ on the graph. We can use either \textit{strongly} connected components (SCC) or \textit{weakly} connected components(WCC) to find the clusters where each cluster should contain all the text regions that form a full location phrase. A strongly connected component is identified if there is a path in \textit{each} direction between each pair of vertices inside the component. A weakly connected component is identified if each node can either reach another node or is reachable from another node. The type of errors for WCC and SCC are different, as that WCC tends to have "adding words" error and scc tends to have "missing words" error. In the experiment section, we use strongly connected component to find the clusters.

The output of connected components are groups of words that can form location phrases. To generate the final location phrase, we need to sort the words in the same set and put them in a sequence. We use a simple method that works well for most of the cases: sort ascendingly for the x-coordinates of the bounding box centers. Other methods can be developed to improve the sorting, and we leave that as future work.

\subsection{Detect Geo-locations of Location Phrases}\label{sec:geocoding}

\begin{figure*}[h]
  \centering
  \includegraphics[width=\linewidth]{images/geomatching.png}
  \caption{Pipeline for geolocalization and entity-matching. The input can either be location phrases or single words extracted from map images. This figure uses the location phrase as an example, but the steps are the same with single words. Based on different inputs, we call the process word-by-word or phrase-by-phrase geolocalization. The output for phrase-by-phrase geocoding is a list of candidate coordinates. We use DB-SCAN to cluster the coordinates and choose the center of the cluster that has the largest number of elements as the geolocalization output. Given the geo-coordinate of the cluster center, we perform entity matching that associates the map with external databases such as GeoLinkedData.  }
  \label{fig:geocoding}
\end{figure*}

\begin{figure}[h]
  \centering
  \includegraphics[width=\linewidth]{images/rdf.png}
  \caption{RDF schema for linked-historical maps. We convert each raster map image into a node in the linked graph, and each node has three properties "type", "sfOverlaps" and "nearby". "sfOverlaps" describes the text regions that are in the map and "nearby" describes the rough location of the map study area. }
  \label{fig:rdf}
\end{figure}

Tavakkol et al. ~\cite{tavakkol2019kartta} introduce a baseline model that concatenates all detected words from a map image into a paragraph, and send the paragraph to Google Geocoding API to produce the geo-coordinates. Although Google Geocoding API works on contemporary data instead of historical data directly, it still carries some advantage: it is a scalable publicly available service and it accounts for minor misspellings and discrepancies. One reason they construct a single paragraph out of the detected words is that they do not have a model to generate phrases out of the individual words. As mentioned earlier, individual words often are not meaningful per se. Furthermore, by calling the Geocoding API only once and choosing the first candidate, they eliminate the need for an algorithm to rank the candidate locations. We improve their framework by generating the location phrases, sending them individually to the Geocoding API, and utilizing a clustering algorithm to produce the geolocation of a map given many candidates that are returned from multiple calls to the Geocoding API. Note that the Geocoding API might also return multiple candidates for a single call. The clustering method we use is DBSCAN~\cite{ester1996density}, which locates the regions that have high density of geolocalized coordinates returned by the Geocoding API. We use the centroid of the cluster that contains the largest number of elements as the final geocoordinates for the map.  The workflow for geocoding is shown in Figure ~\ref{fig:geocoding}. For the sake of comparison, we once send the texts phrase-by-phrase and once word-by-word to the Geocoding API. For example, in the phrase-by-phrase method, the Geocoder takes ``Vermont Street'' as input while in word-by-word method, the Geocoder takes ``Vermont'' and ``Street'' separately and returns two groups of candidates. In the experiments, we compare our phrase-by-phrase geocoding and word-by-word geocoding with the baseline.

\subsection{Matching Entities in Historical Maps to OpenStreetMap} \label{sec:entity_matching}
In the last few decades, researchers have made efforts to structure the contemporary map data into a linked form and have built databases such as LinkedGeoData. Such databases allows queries on contemporary map databases such as OpenStreetMap. However, historical maps are inherently unstructured images. Thus geospatial queries are difficult to run on this type of data. In this paper, we use common vocabularies and the RDF format to represent the automatically generated metadata from historical maps and link the metadata to the existing LinkedGeoData database using the inferred geographical coordinates obtained from Section~\ref{sec:geocoding}. Figure ~\ref{fig:rdf} shows the common vocabularies and the ontology in mapKurator. Section ~\ref{sec:case_study} describes details of the namespaces in the ontology.  This schema provides information for the map image type, coordinates of the map center, location names that are in the map image, and their associated information from other sources (e.g., GeoLinkedData).

The ``sfOverlaps" predicate connects a map subject with a text region object, denoting that the location name is contained in the map image. The  ``seeAlso" property of ``Feature" provides a URI of the location name in another database. When jointly queried with the external database, this property provides a way to backtrack to our Linked-historical-maps dataset. For example, if we are interested in finding maps that contain mountain(s), and LinkedGeoData provides us a set of geo-features with type mountain. We could easily know which ones of them are also contained in the Linked-historical-maps by comparing the URI, and further backtracking to the map image.

\section{Experiments}
In this section, we show the experimental results of mapKurator using several map sources. Our system is evaluated from two aspects: location phrase prediction and map geolocalization. We also provide a case-study of the linked map metadata. This section first describes the datasets used for the experiments, then elaborates on experiment details.

\subsection{Datasets}

\textbf{United States Geological Survey (USGS) topographic maps}~\cite{usgs}: This dataset consists of 15 maps produced by the United States Geological Survey that covers the California State. All the text regions have been manually annotated and transcribed. There are about 293 text regions per map and 4,375 text regions in total. The ground truth geolocation is also provided. This dataset can be used to evaluate both location phrase prediction and map geolocalization tasks.  \\

\noindent\textbf{United Kingdom Ordnance Survey Maps} \footnote{Reproduced with the permission of the National Library of Scotland}~\cite{odmaps} consists of 10 maps published by Ordnance Survey and related bodies, including the War Office (ca. 1840s-1960s). These maps cover some of the regions in the United Kingdom. All the text regions have been manually annotated and transcribed. There are 216 text regions per map on average and 2197 text regions in total. The ground truth geolocation is \textbf{not} provided. Thus this dataset can only be used to evaluate location phrase prediction tasks. \\

\noindent\textbf{NYPL maps}~\cite{nypl}  consists of 500 maps collected by the New York Public Library that Tavakkol et al. ~\cite{tavakkol2019kartta} used to test their geolocalization algorithm. These maps are in the New York region. The text regions are \textbf{not} annotated or transcribed, but the ground truth geolocation is provided. Thus this dataset can only be used to evaluate the map geolocalization task.

\begin{table*}
\centering
  \begin{tabular}{c|c|ccc|ccc}
    \toprule
    \multirow{2}{*}{Dataset } & \multirow{2}{*}{Map Name } &
      \multicolumn{3}{c}{\textbf{Textual Info. Only}} &
      \multicolumn{3}{c}{\textbf{Visual and Textual}}  \\
      & & {Prec.} & {Rec.} & {F1 } & {Prec.} & {Rec.} & {F1 }  \\
      \midrule
    \multirow{3}{*}{USGS } &60-CA-amboy-e1942 & 37.23 & 100.00 & \textbf{54.26} & 93.29 & 56.71 &\textbf{70.54} \\
    & 60-CA-amboy-e1943-rv1943 & 27.01 & 100.00 & \textbf{42.53} & 91.30 & 59.42 & \textbf{71.99} \\
    & 60-CA-modoclavabed-e1886 & 29.03 & 90.55 &\textbf{43.97} & 94.48 & 37.85 &\textbf{54.04}\\
    \midrule
    \midrule
     \multirow{3}{*}{OD } & 103681073 & 50.12 & 97.14 &\textbf{66.12} & 74.52  & 91.43 &\textbf{82.12}\\
    & 103681079 & 50.97 & 97.67 &\textbf{66.98} & 84.88 & 91.19 &\textbf{89.29} \\
    & 103681115 & 62.03 & 94.94 & \textbf{75.03} & 87.34 & 79.75 &\textbf{83.37} \\
    \bottomrule
  \end{tabular}
  \caption{Linkage prediction result on USGS and Ordnance Survey dataset. We use three images from each test set to compare the results of using only textual information versus using both textual and visual information. }
  \label{tab:linkage_prediction}
\end{table*}

\begin{table*}
\centering
  \begin{tabular}{c|c|ccc|ccc|c}
    \toprule
    \multirow{2}{*}{Dataset } & \multirow{2}{*}{Map Name } &
      \multicolumn{3}{c}{\textbf{Duplicate Phrases}} &
      \multicolumn{3}{c}{\textbf{Distinct Phrases}}  & \textbf{\# GT } \\
      & & {Prec.} & {Rec.} & {F1 } & {Prec.} & {Rec.} & {F1 }  &  \textbf{phrase}\\
      \midrule
    \multirow{3}{*}{USGS } &60-CA-amboy-e1942 & 44.72 & 63.16 & \textbf{52.36} & 54.64 & 57.61 &\textbf{56.08} & 114  \\
    & 60-CA-amboy-e1943-rv1943 & 52.45 & 69.03 &\textbf{59.61} & 63.43 & 65.38 & \textbf{64.39} & 155\\
    & 60-CA-modoclavabed-e1886 & 31.28 & 51.85 &\textbf{39.02} & 33.33 & 44.71 &\textbf{38.19}& 108\\
    \midrule
    \midrule
     \multirow{3}{*}{OD } & 103681073 & 74.54 & 85.41 &\textbf{79.61} & 62.50 & 71.42 &\textbf{66.67} & 48 \\
    & 103681079 & 84.00 & 91.30 &\textbf{87.50} & 73.91 & 82.92 &\textbf{78.16} & 69 \\
    & 103681115 & 58.33 & 68.29 & \textbf{62.91} & 55.31 & 63.41 &\textbf{59.08} & 41\\
    \bottomrule
  \end{tabular}
  \caption{Phrase prediction result on USGS and Ordnance Survey dataset. Note that the predicted phrases with the exactly same \textit{content and order} as the ground truth will be considered as a correct prediction. \textit{Duplicate Phrases} means the evaluation considers duplicate phrases in the maps as well. (e.g: "Modoc Lava Beds" may appear twice in the map and we evaluate both cases.) \textit{Distinct Phrases} means we remove the duplicate phraes for evaluation }
  \label{tab:phrase_prediction}
\end{table*}

\subsection{Experimental Result} \label{sec:result}
The main result from mapKurator is the location phrases and the map geolocation, which together, can generate a rich set of linked metadata. We evaluate the performance of mapKurator in generating each result component. For the location phrase generation, we evaluate mapKurator at two stages. The first stage is after pairwise prediction has been made, and the second stage is when the location phrase generation is complete. We show our result on the \textbf{Ordnance Survey} and \textbf{USGS} dataset since these two datasets have manually annotated bounding boxes, transcribed words and phrases. These two datasets have provided the bounding boxes and text content for individual text regions, thus we skip the text detection step and use these information directly as the input for linkage prediction model. Table~\ref{tab:linkage_prediction} shows the linkage prediction performance on these two datasets. In this table, we compare the result of using only textual information and the result of using textual and visual information. The precision is defined as $p_l = TP_l / (TP_l + FP_l)$, recall is defined as $r_l = TP_l / (TP_l + FN_l)$ and $F1 = 2p_l \cdot r_l/(p_l + r_l)$, where $TP_l$ is the number of predicted linkages that have been correctly classified, $FP_l$ is the number of predicted linkages that have been incorrectly classified, and $FN_l$ is the number of linkages that have been missed from prediction. We can see that $F1$ score is improved by a large margin when visual information is added. We also notice that when using only textual information, the recall is high, but the precision is low. This may be due to the fact that if only textual information is considered, the model lacks information about context the text regions belong to, which is similar to predicting the linkage relation on a blank background image. Thus, the network predicts relatively more positive linkages than the ground truth and incurs a high false-positive rate. By adding visual information, most of the false positives can be removed. Table~\ref{tab:phrase_prediction} shows the performance for full location phrase generation. The results are generated on the map-wise level by calculating how many location phrases have been correctly recognized.
We show the numerical results for two cases: 1) For \textit{Duplicate Phrase}, we include duplicate phrases in evaluation 2) For \textit{Distinct Phrases}, we evaluates the distinct phrase correctness. For example, if the ground truth contains two "Modoc Lava Beds" and one "Black Crater", the prediction extracts two "Modoc Lava Beds" succesfuly but no "Black Crater". In the first metric, the recall is 2/3 and in the second metric the recall is 1/2.

We evaluated the performance on geolocalization by calculating the distance deviated from ground truth for the predicted latitude and longitude coordinates. In table ~\ref{tab:usgs_geolocalization_prediction}, We compare our \texttt{word-by-word} and \texttt{phrase-by-phrase} geocoding method described in Section ~\ref{sec:geocoding} with the \texttt{word-to-paragraph} method introduced in ~\cite{tavakkol2019kartta} on USGS dataset.  We show the error in both \texttt{kilometers} and in \texttt{scale}. The error metrics are defined bellow:

\begin{align}
    & Err_{km}(\textbf{g},\textbf{p}) = Haversine(\textbf{g},\textbf{p}) = \sum_i 2\arcsin( \sqrt{h_i}) \\
    & h_i = \sin^2(\frac{g^{lat}_i - p^{lat}_i}{2}) + \cos(g^{lat}_i)\cos(^p{lat}_i)\sin^2(\frac{g^{lng}_i - p^{lng}_i}{2})
\end{align}
where $\textbf{g}$ is the ground truth location and $\textbf{p}$ is the prediction.
The reason to use \textit{Haversine} distance instead of \textit{Euclidean} distance is that the Haversine distance measures the great-circle distance on a sphere, which is more appropriate in our setting while  Euclidean distance measures the straight-line distance between two points.
\begin{align}
    & Err_{scale}(\textbf{g},\textbf{p}) = \frac{Err_{km}}{Haversine(t_{min},t_{max})}
\end{align}
where $t_{min}$ is the point at the corner of the map plot area that has the smallest latitude and longitude value, similarly, $t_{max}$ is the point with the largest value. Thus if $Err_{scale}$ is larger than 1, the map image does not cover the predicted geolocalization coordinates.

We also evaluated geolocalization on a much larger dataset -- the \textbf{NYPL} dataset that contains 500 historical map images. This dataset does not have bounding boxes information for individual text regions, so we used Google Vision API for text detection and feed the detection result to the downstream models. Since the number of map images is large, we could not list the result for each map. We instead provide the error distribution histogram in Figure ~\ref{fig:nypl_hist}. The \texttt{word-by-word} and \texttt{phrase-by-phrase} do not have as many large errors as baseline \texttt{word-to-paragraph} method. Also, the number of map images that the model fails to handle got decreased with \texttt{word-by-word} and \texttt{phrase-by-phrase} method.

\begin{table*}
\setlength\tabcolsep{4 pt}
\centering
  \begin{tabular}{c|c|cc|cc|cc|cc}
    \toprule
    & \multirow{2}{*}{Map Name }  &
      \multicolumn{2}{c}{\textbf{Wrd2Paragraph }} &
      \multicolumn{2}{c}{\textbf{WrdByWrd(Ours)}}  &
       \multicolumn{2}{c}{\textbf{PhrasByPhras(Ours)}}  &
      \multicolumn{2}{c}{\textbf{Ground Truth}}  \\
     & & {Lat.} & {Lng.}  & {Lat.} & {Lng.}& {Lat.} & {Lng.} & {Lat.} & {Lng.}  \\
      \midrule
    \multirow{3}{*}{\shortstack[l]{\textbf{Pred.} \\($^{\circ}$) }} & 60-CA-amboy-e1942 & 38.57    &-121.48 & 33.94& -116.83 & 34.25 &    -116.24 & 34.50    &-115.50 \\
   &  60-CA-amboy-e1943-rv1943 & 38.57 & -121.48&  33.97& -116.78 & 34.24 & -116.18 & 34.50 &    -115.50 \\
   &  60-CA-modoclavabed-e1886 & - & - & 37.99    & -121.80 & 41.16&     -121.54 & 41.50 &    -121.50  \\
    \midrule
    \midrule
    & & km & scale& km & scale& km & scale& km & scale \\
    \midrule
    \multirow{3}{*}{ \shortstack[l]{\textbf{Error} }} & 60-CA-amboy-e1942 &699.89  & 4.85& 137.23& 0.95 &\textbf{ 73.38} & \textbf{0.51}& N/A& N/A\\
   &  60-CA-amboy-e1943-rv1943 & 699.89 & 4.85&    131.60 & 0.91&\textbf{68.78}& \textbf{0.48 }& N/A & N/A\\
   &  60-CA-modoclavabed-e1886 &N/A & N/A& 391.14 & 2.82 & \textbf{37.95} & \textbf{0.27}& N/A & N/A\\
    \bottomrule
  \end{tabular}
  \caption{Geolocalization result on USGS maps. The upper rows are the prediction results from different methods, and the bottom rows are the errors deviated from ground truth. We use the \textbf{Haversine distance} in kilometers and in map scale as the error metric, thus the lower the better. "-" in the entry means no geolocalization result was returned by the model.}
  \label{tab:usgs_geolocalization_prediction}
\end{table*}

\begin{figure}[t]
\begin{center}
\includegraphics[width=\linewidth]{images/nypl_hist.pdf}
\end{center}
   \caption{Comparison of the histogram distribution for word2Paragraph ~\cite{tavakkol2019kartta}, word-by-word encoding (ours) and phrase-by-phrase encoding (ours) on the NYPL dataset. Left: word2Paragraph; Middle: word-by-word; Right: phrase-by-phrase. The x-axis denotes the error. It is clear to see that phrase-by-phrase and word-by-word do not have extreme large errors compared with word2Paragraph. }
\label{fig:nypl_hist}
\end{figure}

\subsection{Case Study of Linked Map Metadata}\label{sec:case_study}

In Section ~\ref{sec:entity_matching}, we presented the RDF schema adopted for linked-historical maps, and explained that the structure enables us to jointly query with other external databases. In this section, we provide an example of a spatial query using SPARQL and show the RDF schema graph in XML in Figure ~\ref{fig:xml}.

\begin{figure}[h]
\begin{center}
\includegraphics[width=1.05\linewidth]{images/rdf_xml.png}
\end{center}
   \caption{Linked historical map in RDF with XML syntax}
\label{fig:xml}
\end{figure}
Based on this, we run a query that finds the historical maps which contains mountain(s) higher than 1km. From GeoLinkedData, the query first select all the mountains and filter out the ones whose elevations are less than one kilometer. Then it checks the \texttt{seeAlso} predicate and backtrack to the text regions that are associated with those mountains. At last, our system returns the names of the maps that overlaps with those text regions.
\begin{figure}[h]
\begin{center}
\includegraphics[width=0.9\linewidth]{images/sparql.png}
\end{center}
   \caption{Sample SPARQL query to retrieve map names that contain peaks higher than 1km}
\label{fig:sparql}
\end{figure}

\subsection{Error Analysis}
Since geolocalization and entity linking heavily depend on the result of phrase generation, it is crucial to analyze the errors in the phrase generation step. In Table ~\ref{tab:phrase_prediction}, we have already shown the precision and recall of the generated phrases, and we can dig deeper into that by looking at the cases that caused the error. There are mainly three types of error: 1) pairwise linkage prediction error that happens during the text linking step 2) linkage graph generation error that depends on the method of graph construction 3) element sorting error for deciding the correct sequence of the text regions that belong to the same phrase. The first type of error has been analyzed in previous sections and the second type of error depends on the graph construction. Thus we analyze the third type of error in this section. In Table ~\ref{tab:error_analysis}, we show the precision and recall for both unordered word sets (i.e., elements in connected component) and phrase prediction result. The values in the ``Unordered" column can be seen as the upper bound of the phrase prediction given the current graph construction setting since it ignores the sequence of the word in the location phase, which is the same as the perfect ordering of words. By comparing these two columns, we could see that our simple ordering method, which sorts according to the x-axis coordinate works fine for this type of maps, as the difference between ``Unordered" and "Ordered" is pretty small. These two columns analyzed the third type of error.

\begin{table}[h]
\centering
\setlength\tabcolsep{4 pt}
  \begin{tabular}{c|ccccc}
    \toprule
    Err (km) & Unordered & Ordered & \# miss & \# add & \#GT \\
    \midrule
    amboy-e1942 & 46.58/65.79 & 44.72/63.16 & 89& 0& 114\\
    amboy-rv1943 & 52.94/69.68 &52.45/69.03 & 95 &4 & 155\\
    modoclavabed & 31.28/51.85 & 31.28/51.85 & 123 &0 & 108\\
    \bottomrule
  \end{tabular}
  \caption{Error analysis for generated phrases. In the table, ``Unordered" column shows the precision and recall when we do not consider the phrase ordering, ``Ordered" means we consider both the words and the ordering for the phrase. \#miss counts the number of errors caused by missing words,  \#add counts the number of errors caused by adding words and \#GT means the number of ground truth phrases contained in the map. }
  \label{tab:error_analysis}
  \vspace{-2em}
\end{table}

\section{Deployment}
Tavakkol et al. ~\cite{tavakkol2019kartta} introduce an open-source project, Kartta Labs, to organize the world's historical maps and make them universally accessible and useful. They define a modular design for the project to let it thrive on a collaborative community development effort. The current study is done in collaboration with the Kartta Labs team and with the purpose of being deployed in the project. We, specifically, design and implement the Geolocalizer and Linker modules of Kartta Labs. To learn more, see  https://github.com/kartta-labs/Project.

To use our model in production, we plan to deploy it on Google Cloud Functions, which is an event-driven serverless compute platform. Our Cloud Function deployment runs the model as a service and makes it accessible through HTTP requests. In our preliminary implementation, the map images are stored on Google Cloud Storage and each image has been assigned with a URI. The model cloud function takes the URI of the map image as the input. It then sends it to the Google Vision API to retrieve the textual information of the map. The response from the Vision API is preprocessed and sent to the model for generating location phrases. The generated phrases are sent to Google Geocoding API. Finally, the geolocation of the map is determined based on the candidate locations returned from the Geocoding API. The generated phrases, and the geolocation of the map is returned to the client in a JSON response which can be used for downstream tasks such as entity linking.

After successful deployment and productionization, our implementation will replace the word2paragraph geolocalizer in Kartta Labs Warper web application. Warper is a web application that lets the users upload a historical map and georectify it by finding control points on the historical map and corresponding points on a contemporary base map. Once the user uploads a map, the map is sent to the geolocalizer. The base map is loaded according to the estimated geolocation of the historical map. This lets the user immediately start the georectification process without the need to navigate the world map and load the base map in an appropriate location.

\section{Related Work}
\subsection{Text Detection and Text Linking}
Historical map processing has attracted a lot of attention in recent years\cite{chiang2020using,li2019generating,chiang2016assessing}, but the textual information extraction on maps still remains a challenging problem. Many networks have been developed to detect and recognize text content from scene images such as EAST ~\cite{zhou2017east} and TextBox++ ~\cite{liao2018textboxes++} ; however they do not provide any information about the relation between detected words. Some words can be joined together to form phrases. The task of linking related words can be seen as finding similar features in a hidden embedding space. Deep metric learning aims to explicitly capture the similarity among the features and learn an embedding space where similar features are grouped together, and dissimilar features are quite far away. Siamese network ~\cite{guo2017learning, taigman2014deepface} and Triplet network ~\cite{hoffer2015deep} are the most widely used structures for the deep metric learning models. Siamese network computes pairwise similarity and tries to push the similar pairs closer and dissimilar pairs far away. In Triplet networks, instead of comparing \textit{two} feature points, it compares 3-element tuple. Another work that is closely related to text linking is weakly supervised segmentation ~\cite{khoreva2017simple}, where only partial labels are given. We take advantage of both deep metric learning and weakly supervised segmentation to generate linking results.

\subsection{Geolocalization based on Text Content }
Geolocalization of historical map is estimating the location of the map on Earth. Some work has been done to geolocalize the social network users based on the text content that the user posted ~\cite{ferrari2011extracting, xu2015geolocalized}, but not much has been done to geolocalize map images. The work that is most closely related to ours is ~\cite{tavakkol2019kartta}, which utilized Google NLP API and Google Geocoding API to produce latitude and longitude coordinates. This work takes in all the location-related words detected from the map and concatenates them into paragraphs. The Geocoding API reads the paragraph as input and produces one location. We proposed word-by-word and phrase-by-phrase geocoding that takes words or phrases instead to generate candidate locations, then further clusters the candidate locations to produce the geolocalization output.

\section{Discussion and Future Work}
This paper presented an end-to-end approach to generate rich, linked metadata from historical maps. In addition to the complete approach that addresses the real-world problem of finding and indexing historical map images, our contribution is two-fold: First, we proposed a model to harness both textual information and visual information to generate location phrases. Second, we designed a method to geolocalize a map image using the location phrases generated from the first step. The proposed method constitutes an important building block for efficient, large-scale information extraction processing chains from historical map archives. We plan to investigate sophisticated ontologies to represent the metadata so that the metadata can support more types of queries and record important information, such as the provenance information. We also plan to expand the phrase generation module to automatically identify the source (e.g., map publisher) and temporal information from a map image.

\section*{Acknowledgements}
This material is based upon work supported in part by the National Science Foundation under Grant Nos. IIS 1564164 (to the University of
Southern California) and IIS 1563933 (to the University of Colorado at Boulder), NVIDIA Corporation, and the USC Undergraduate Research Associates Program.
\bibliographystyle{ACM-Reference-Format}

\section{Introduction}
The \textit{proceedings} are the records of a conference.
ACM seeks to give these conference by-products a uniform,
high-quality appearance.  To do this, ACM has some rigid
requirements for the format of the proceedings documents: there
is a specified format (balanced  double columns), a specified
set of fonts (Arial or Helvetica and Times Roman) in
certain specified sizes (for instance, 9 point for body copy),
a specified live area (18 $\times$ 23.5 cm [7" $\times$ 9.25"]) centered on
the page, specified size of margins (2.54cm [1"] top and
bottom and 1.9cm [.75"] left and right; specified column width
(8.45cm [3.33"]) and gutter size (.083cm [.33"]).

The good news is, with only a handful of manual
settings\footnote{Two of these, the {\texttt{\char'134 numberofauthors}}
and {\texttt{\char'134 alignauthor}} commands, you have
already used; another, {\texttt{\char'134 balancecolumns}}, will
be used in your very last run of \LaTeX\ to ensure
balanced column heights on the last page.}, the \LaTeX\ document
class file handles all of this for you.

The remainder of this document is concerned with showing, in
the context of an ``actual'' document, the \LaTeX\ commands
specifically available for denoting the structure of a
proceedings paper, rather than with giving rigorous descriptions
or explanations of such commands.

\section{The Body of The Paper}
Typically, the body of a paper is organized
into a hierarchical structure, with numbered or unnumbered
headings for sections, subsections, sub-subsections, and even
smaller sections.  The command \texttt{{\char'134}section} that
precedes this paragraph is part of such a
hierarchy.\footnote{This is the second footnote.  It
starts a series of three footnotes that add nothing
informational, but just give an idea of how footnotes work
and look. It is a wordy one, just so you see
how a longish one plays out.} \LaTeX\ handles the numbering
and placement of these headings for you, when you use
the appropriate heading commands around the titles
of the headings.  If you want a sub-subsection or
smaller part to be unnumbered in your output, simply append an
asterisk to the command name.  Examples of both
numbered and unnumbered headings will appear throughout the
balance of this sample document.

Because the entire article is contained in
the \textbf{document} environment, you can indicate the
start of a new paragraph with a blank line in your
input file; that is why this sentence forms a separate paragraph.

\subsection{Type Changes and Special Characters}
We have already seen several typeface changes in this sample.  You
can indicate italicized words or phrases in your text with
the command \texttt{{\char'134}textit}; emboldening with the
command \texttt{{\char'134}textbf}
and typewriter-style (for instance, for computer code) with
\texttt{{\char'134}texttt}.  But remember, you do not
have to indicate typestyle changes when such changes are
part of the \textit{structural} elements of your
article; for instance, the heading of this subsection will
be in a sans serif\footnote{A third footnote, here.
Let's make this a rather short one to
see how it looks.} typeface, but that is handled by the
document class file. Take care with the use
of\footnote{A fourth, and last, footnote.}
the curly braces in typeface changes; they mark
the beginning and end of
the text that is to be in the different typeface.

You can use whatever symbols, accented characters, or
non-English characters you need anywhere in your document;
you can find a complete list of what is
available in the \textit{\LaTeX\
User's Guide}\cite{Lamport:LaTeX}.

\subsection{Math Equations}
You may want to display math equations in three distinct styles:
inline, numbered or non-numbered display.  Each of
the three are discussed in the next sections.

\subsubsection{Inline (In-text) Equations}
A formula that appears in the running text is called an
inline or in-text formula.  It is produced by the
\textbf{math} environment, which can be
invoked with the usual \texttt{{\char'134}begin. . .{\char'134}end}
construction or with the short form \texttt{\$. . .\$}. You
can use any of the symbols and structures,
from $\alpha$ to $\omega$, available in
\LaTeX\cite{Lamport:LaTeX}; this section will simply show a
few examples of in-text equations in context. Notice how
this equation: \begin{math}\lim_{n\rightarrow \infty}x=0\end{math},
set here in in-line math style, looks slightly different when
set in display style.  (See next section).

\subsubsection{Display Equations}
A numbered display equation -- one set off by vertical space
from the text and centered horizontally -- is produced
by the \textbf{equation} environment. An unnumbered display
equation is produced by the \textbf{displaymath} environment.

Again, in either environment, you can use any of the symbols
and structures available in \LaTeX; this section will just
give a couple of examples of display equations in context.
First, consider the equation, shown as an inline equation above:
\begin{equation}\lim_{n\rightarrow \infty}x=0\end{equation}
Notice how it is formatted somewhat differently in
the \textbf{displaymath}
environment.  Now, we'll enter an unnumbered equation:
\begin{displaymath}\sum_{i=0}^{\infty} x + 1\end{displaymath}
and follow it with another numbered equation:
\begin{equation}\sum_{i=0}^{\infty}x_i=\int_{0}^{\pi+2} f\end{equation}
just to demonstrate \LaTeX's able handling of numbering.

\subsection{Citations}
Citations to articles \cite{bowman:reasoning, clark:pct, braams:babel, herlihy:methodology},
conference
proceedings \cite{clark:pct} or books \cite{salas:calculus, Lamport:LaTeX} listed
in the Bibliography section of your
article will occur throughout the text of your article.
You should use BibTeX to automatically produce this bibliography;
you simply need to insert one of several citation commands with
a key of the item cited in the proper location in
the \texttt{.tex} file \cite{Lamport:LaTeX}.
The key is a short reference you invent to uniquely
identify each work; in this sample document, the key is
the first author's surname and a
word from the title.  This identifying key is included
with each item in the \texttt{.bib} file for your article.

The details of the construction of the \texttt{.bib} file
are beyond the scope of this sample document, but more
information can be found in the \textit{Author's Guide},
and exhaustive details in the \textit{\LaTeX\ User's
Guide}\cite{Lamport:LaTeX}.

So far, this article has shown only the plainest form
of the citation command, using \texttt{{\char'134}cite}.

\subsection{Tables}
Because tables cannot be split across pages, the best
placement for them is typically the top of the page
nearest their initial cite.  To
ensure this proper ``floating'' placement of tables, use the
environment \textbf{table} to enclose the table's contents and
the table caption.  The contents of the table itself must go
in the \textbf{tabular} environment, to
be aligned properly in rows and columns, with the desired
horizontal and vertical rules.  Again, detailed instructions
on \textbf{tabular} material
is found in the \textit{\LaTeX\ User's Guide}.

Immediately following this sentence is the point at which
Table 1 is included in the input file; compare the
placement of the table here with the table in the printed
dvi output of this document.

\begin{table}
\centering
\caption{Frequency of Special Characters}
\begin{tabular}{|c|c|l|} \hline
Non-English or Math&Frequency&Comments\\ \hline
\O & 1 in 1,000& For Swedish names\\ \hline
$\pi$ & 1 in 5& Common in math\\ \hline
\$ & 4 in 5 & Used in business\\ \hline
$\Psi^2_1$ & 1 in 40,000& Unexplained usage\\
\hline\end{tabular}
\end{table}

To set a wider table, which takes up the whole width of
the page's live area, use the environment
\textbf{table*} to enclose the table's contents and
the table caption.  As with a single-column table, this wide
table will "float" to a location deemed more desirable.
Immediately following this sentence is the point at which
Table 2 is included in the input file; again, it is
instructive to compare the placement of the
table here with the table in the printed dvi
output of this document.

\begin{table*}
\centering
\caption{Some Typical Commands}
\begin{tabular}{|c|c|l|} \hline
Command&A Number&Comments\\ \hline
\texttt{{\char'134}alignauthor} & 100& Author alignment\\ \hline
\texttt{{\char'134}numberofauthors}& 200& Author enumeration\\ \hline
\texttt{{\char'134}table}& 300 & For tables\\ \hline
\texttt{{\char'134}table*}& 400& For wider tables\\ \hline\end{tabular}
\end{table*}

\subsection{Theorem-like Constructs}
Other common constructs that may occur in your article are
the forms for logical constructs like theorems, axioms,
corollaries and proofs.  There are
two forms, one produced by the
command \texttt{{\char'134}newtheorem} and the
other by the command \texttt{{\char'134}newdef}; perhaps
the clearest and easiest way to distinguish them is
to compare the two in the output of this sample document:

This uses the \textbf{theorem} environment, created by
the \texttt{{\char'134}newtheorem} command:
\newtheorem{theorem}{Theorem}
\begin{theorem}
Let $f$ be continuous on $[a,b]$.  If $G$ is
an antiderivative for $f$ on $[a,b]$, then
\begin{displaymath}\int^b_af(t)dt = G(b) - G(a).\end{displaymath}
\end{theorem}

The other uses the \textbf{definition} environment, created
by the \texttt{{\char'134}newdef} command:
\newdef{definition}{Definition}
\begin{definition}
If $z$ is irrational, then by $e^z$ we mean the
unique number which has
logarithm $z$: \begin{displaymath}{\log_e^z = z}\end{displaymath}
\end{definition}

Two lists of constructs that use one of these
forms is given in the
\textit{Author's  Guidelines}.

There is one other similar construct environment, which is
already set up
for you; i.e. you must \textit{not} use
a \texttt{{\char'134}newdef} command to
create it: the \textbf{proof} environment.  Here
is a example of its use:
\begin{proof}
Suppose on the contrary there exists a real number $L$ such that
\begin{displaymath}
\lim_{x\rightarrow\infty} \frac{f(x)}{g(x)} = L.
\end{displaymath}
Then
\begin{displaymath}
l=\lim_{x\rightarrow c} f(x)
= \lim_{x\rightarrow c}
\left[ g{x} \cdot \frac{f(x)}{g(x)} \right ]
= \lim_{x\rightarrow c} g(x) \cdot \lim_{x\rightarrow c}
\frac{f(x)}{g(x)} = 0\cdot L = 0,
\end{displaymath}
which contradicts our assumption that $l\neq 0$.
\end{proof}

Complete rules about using these environments and using the
two different creation commands are in the
\textit{Author's Guide}; please consult it for more
detailed instructions.  If you need to use another construct,
not listed therein, which you want to have the same
formatting as the Theorem
or the Definition\cite{salas:calculus} shown above,
use the \texttt{{\char'134}newtheorem} or the
\texttt{{\char'134}newdef} command,
respectively, to create it.

\subsection*{A Caveat for the \TeX\ Expert}
Because you have just been given permission to
use the \texttt{{\char'134}newdef} command to create a
new form, you might think you can
use \TeX's \texttt{{\char'134}def} to create a
new command: \textit{Please refrain from doing this!}
Remember that your \LaTeX\ source code is primarily intended
to create camera-ready copy, but may be converted
to other forms -- e.g. HTML. If you inadvertently omit
some or all of the \texttt{{\char'134}def}s recompilation will
be, to say the least, problematic.

\section{Conclusions}
This paragraph will end the body of this sample document.
Remember that you might still have Acknowledgements or
Appendices; brief samples of these
follow.  There is still the Bibliography to deal with; and
we will make a disclaimer about that here: with the exception
of the reference to the \LaTeX\ book, the citations in
this paper are to articles which have nothing to
do with the present subject and are used as
examples only.

\section{Acknowledgements}
This section is optional; it is a location for you
to acknowledge grants, funding, editing assistance and
what have you.  In the present case, for example, the
authors would like to thank Gerald Murray of ACM for
his help in codifying this \textit{Author's Guide}
and the \textbf{.cls} and \textbf{.tex} files that it describes.

\bibliographystyle{abbrv}

redpajama_set_name: RedPajamaArXiv


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text:
Extinction Rebellion: Youth perspective about climate change to protect the future
Sun, 06 Nov 2022 - 02:50 GMT
File: Aiden Less/ taken by Amira Nour.
Extinction Rebellion Youth is the autonomous youth wing of the global environmental movement Extinction a Rebellion (XR), made up of activists under the age of 30.
Aiden Less said to Egypt Today that Extinction Rebellion Youth works to counteract the impact of climate change focusing on non-violent direct action.
''Climate change and social justice were bringing in the impact of climate change on marginalized groups so together they form what we describe as climate justice and fighting for climate justice will help push the entire movement forward'' Less said to Egypt Today.
As a youth environmental activist Less admitted that one of the major changes that he has seen over the past years is centring youth activist.
''We have always been part of it, but there is a shift that I have witnessed recently which is youth being the focal point. I believe the role of youth is to really push the conversation forward, climate change and climate justice are always number one priority to governments and corporations so we have to work consistently.''
Less added that attracting the attention of the international community to climate change crisis is almost impossible task but going back to the point of intersectionality we can use that to interface what is going on in the world with climate justice so taking what is relevant now and trying to interface it with climate justice is a great way to insure that people are talking about it.
''My recommendations to COP27 are to put youth voices in the center, making sure that youth committees are intersectional, I hope that COP27 brings climate change and climate justice back to the minds of people, media and the whole international community''
Less further explained that Legislators, politicians, corporate officials, youth and local leaders will work together to make tangible change with the passion in the mindset that comes from COP27.
As for Egypt hosting COP27 Less said that he thinks that it is really a good transition and a necessary step for COP27 to be held in a place like Egypt.
''We have to learn the impact of climate change on other places like Egypt, so having a conversation about climate change in Egypt is really a great thing. My message to the international community is that everybody should get involved, everyone has a role to play especially youth so my message to everyone is get involved, take your friend, classmate or co-worker and start building that community that solve climate change crisis, use all your talents to make the maximum change you can make and try to push the conversation forward to make progress, I think this is one of the main reasons why youth climate justice activism is important."
Kate Spaulding and Sasha Rotko explained how people view climate change regionally expressing their desire to attend COP 28 and the need for systemic change.
Spaulding said that Extinction Rebellion Youth has the stated aim of using non violent civil disobedience to compel government action to avoid tipping points in the climate system, biodiversity loss and the risk of social and ecological collapse.
''Diverse voices need to be heard' Spaulding and Rotko said.
Spaulding recounted those places where COP is hosted makes a difference in the conversations.
''You have a voice, and it can be heard even though your actions may differ whether larger actions or smaller actions'' Rotko said.
Spaulding added that disruption to the system is the way to change and Youth perspective about climate change will protect the future.
Aiden Less
Extinction Rebellion Youth


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