LISAt: Language-Instructed Segmentation Assistant for Satellite Imagery
Jerome Quenum, Wen-Han Hsieh, Tsung-Han (Patrick) Wu, Ritwik Gupta, Trevor Darrell, David Chan
Segmentation models can recognize a pre-defined set of objects in images. However, segmentation models capable of "reasoning" over complex user queries that implicitly refer to multiple objects of interest remain underexplored, especially in the geospatial domain. Recent advances in "reasoning segmentation"---generating segmentation masks from complex, implicit query text---demonstrate the potential of vision-language models (VLMs) to reason across an open domain of objects. Yet, our experiments reveal that these models struggle when applied to the unique challenges of remote-sensing imagery. To address this gap, we introduce a new dataset which consists of: GRES, a curated geospatial reasoning-segmentation dataset with 27,615 annotations across 9,205 images, and PreGRES, a collection of existing datasets to make up a large-scale multimodal pretraining corpus with over 1M question-answer pairs across 119,279 images. We propose an initial benchmark model, LISAt, a VLM for geospatial analysis that can describe complex remote-sensing scenes, answer detailed queries, and segment objects based on natural-language prompts. LISAt establishes a strong initial geospatial benchmark, outperforming prior foundation models such as RS-GPT4V by 10.04\% (BLEU-4) on visual description tasks and surpassing open-domain models on geospatial reasoning segmentation by 143.36\% (gIoU). Our model, dataset, and code are available on our project page: https://lisat-bair.github.io/LISAt/.