Functional Map of the World (fMoW) is a dataset that aims to inspire the development of machine learning models capable of predicting the functional purpose of buildings and land use from temporal sequences of satellite images and a rich set of metadata features.
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xView is one of the largest publicly available datasets of overhead imagery. It contains images from complex scenes around the world, annotated using bounding boxes. It contains over 1M object instances from 60 different classes.
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RoadTracer is a dataset for extraction of road networks from aerial images. It consists of a large corpus of high-resolution satellite imagery and ground truth road network graphs covering the urban core of forty cities across six countries. For each city, the dataset covers a region of approximately 24 sq km around the city center. The satellite imagery is obtained from Google at 60 cm/pixel resolution, and the road network from OSM.
29 PAPERS • 2 BENCHMARKS
The xBD dataset contains over 45,000KM2 of polygon labeled pre and post disaster imagery. The dataset provides the post-disaster imagery with transposed polygons from pre over the buildings, with damage classification labels.
29 PAPERS • 2 BENCHMARKS
CrisisMMD is a large multi-modal dataset collected from Twitter during different natural disasters. It consists of several thousands of manually annotated tweets and images collected during seven major natural disasters including earthquakes, hurricanes, wildfires, and floods that happened in the year 2017 across different parts of the World. The provided datasets include three types of annotations.
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MEDIC is a large social media image classification dataset for humanitarian response consisting of 71,198 images to address four different tasks in a multi-task learning setup. It consists data from several data sources such as CrisisMMD, data from AIDR and Damage Multimodal Dataset (DMD).
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Multi-Sensor All Weather Mapping (MSAW) is a dataset and challenge, which features two collection modalities (both SAR and optical). The dataset and challenge focus on mapping and building footprint extraction using a combination of these data sources. MSAW covers 120 km^2 over multiple overlapping collects and is annotated with over 48,000 unique building footprints labels, enabling the creation and evaluation of mapping algorithms for multi-modal data.
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