SECOND is a well-annotated semantic change detection dataset. To ensure data diversity, we firstly collect 4662 pairs of aerial images from several platforms and sensors. These pairs of images are distributed over the cities such as Hangzhou, Chengdu, and Shanghai. Each image has size 512 x 512 and is annotated at the pixel level. The annotation of SECOND is carried out by an expert group of earth vision applications, which guarantees high label accuracy. For the change category in the SECOND dataset, we focus on 6 main land-cover classes, i.e. , non-vegetated ground surface, tree, low vegetation, water, buildings and playgrounds , that are frequently involved in natural and man-made geographical changes. It is worth noticing that, in the new dataset, non-vegetated ground surface ( n.v.g. surface for short) mainly corresponds to impervious surface and bare land. In summary, these 6 selected land-cover categories result in 30 common change categories (including non-change ). Through the
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ChangeSim is a dataset aimed at online scene change detection (SCD) and more. The data is collected in photo-realistic simulation environments with the presence of environmental non-targeted variations, such as air turbidity and light condition changes, as well as targeted object changes in industrial indoor environments. By collecting data in simulations, multi-modal sensor data and precise ground truth labels are obtainable such as the RGB image, depth image, semantic segmentation, change segmentation, camera poses, and 3D reconstructions. While the previous online SCD datasets evaluate models given well-aligned image pairs, ChangeSim also provides raw unpaired sequences that present an opportunity to develop an online SCD model in an end-to-end manner, considering both pairing and detection. Experiments show that even the latest pair-based SCD models suffer from the bottleneck of the pairing process, and it gets worse when the environment contains the non-targeted variations.
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TAMPAR is a real-world dataset of parcel photos for tampering detection with annotations in COCO format. For details see the paper and for visual samples the project page. Features are:
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This dataset contains the ground truth for urban changes occurred in Mariupol, Ukraine for the time frame 2017-2020. This is useful for transferring the urban change monitoring network ERCNN-DRS (https://github.com/It4innovations/ERCNN-DRS_urban_change_monitoring) to that region.
The dataset comprises patches of size 512x512 pixels collected from Sentinel-2 L2A satellite mission. All reported forest fires are located in California. For each area of interest, two images are provided: pre-fire acquisition and post-fire acquisition. Each image is composed of 12 different channels, collecting information from the visible spectrum, infrared and ultrablue.
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