<p align=center>`Awesome Remote Sensing Change Detection`</p>

List of datasets, codes, and contests related to remote sensing change detection.

Dateset
Code
- Multispectral
  - Traditional Method
  - Deep Learning
    - 2D
    - 3D
- SAR
- Hyperspectral
Contest
Reference

Dateset

Multispectral

With Label

2024.Hi-CNA dataset
The Hi-CNA is a high-resolution remote sensing dataset for cropland non-agriculturalization (CNA) tasks, featuring high-quality semantic and change annotations for cropland. It covers over 1100 km² in Hebei, Shanxi, Shandong, and Hubei provinces in China, areas known for diverse crop planting patterns. The dataset includes two temporal phases: 2015-2017 and 2020-2022, capturing multiple crop phenological periods.The Hi-CNA dataset is derived from multispectral GF-2 fusion images with a 0.8m spatial resolution across four bands (visible and near-infrared). All images are cropped to 512x512, resulting in 6797 pairs of dual-temporal images with corresponding annotations. Paper: Sun et al., 2024
2023.ChangeNet
The ChangeNet dataset comprises 31,000 pairs of multi-temporal images, each with a resolution of 0.3 meters. It encapsulates a diverse array of complex scenes from 100 different cities. Additionally, the dataset includes six pixel-level annotated categories, which encompass the classes of "building," "farmland," "bareland," "water," "road," and "unchanged". Paper: Ji et al., 2023
2023.SMARS (Simulated Multimodal Aerial Remote Sensing) dataset
SMARS is a large-scale synthetic dataset, comprising pairs of scenarios simulating urban changes, designed for training and validating change detection applications, as well as urban segmentation and building extraction tasks. The dataset simulates the terrains of two European cities: Paris and Venice, generating scenario pairs named SParis and SVenice, each accompanied by ortho-images and Digital Surface Models (DSMs). Changes between the before and after scenarios are simulated, with the latter having more buildings, less green space, and some simulated demolished buildings. To enhance similarity with real data, input images are further processed into ortho-photos and digital elevation models through a standard photogrammetric process, including different lighting conditions and other effects, such as blurred building boundaries. Reference ground truth maps are directly used for training and validation of change detection, building extraction, and urban segmentation tasks. The data is rendered at two Ground Sampling Distances (GSDs) of 30cm and 50cm. Each tile, from left to right, includes: optical image, DSM, semantic and building masks. For change detection, the difference between two events is used as the ground truth. Paper: Reyes et al., 2023
2023.HRCUS-CD (High-Resolution Complex Urban Scene Change Detection)
The proposed High-Resolution Complex Urban Scene Change Detection (HRCUS-CD) dataset consists of 11,388 pairs of cropped high-resolution remote sensing images. The image size is 256 × 256 pixels with a resolution of 0.5 meters. The dataset includes over 12,000 annotated instances of changes. The data was collected in Zhuhai, China. It contains two main acquisition areas from two image sources: the first is mainly the urban built-up area, with a time span from 2019 to 2022. Considering the short time interval and the fact that this area is mostly built-up, the building changes’ areas are small. The second area spans from 2010 to 2018, and contains farmland and mountains, with a small number of old civil houses and buildings in the early period, and the area of building change is large later. These two types of high-resolution RSIs focus on built-up areas and new urban areas. Paper: Zhang et al., 2023
2023.GVLM
The Global Very-High-Resolution Landslide Mapping (GVLM) dataset is the first large-scale and open-source VHR landslide mapping dataset. It includes 17 bitemporal very-high-resolution imagery pairs with a spatial resolution of 0.59 m acquired via Google Earth service. Each sub-dataset contains a pair of bitemporal images and the corresponding ground-truth map. The landslide sites in different geographical locations have various sizes, shapes, occurrence times, spatial distributions, phenology states, and land cover types, resulting in considerable spectral heterogeneity and intensity variations in the remote sensing imagery. The GVLM dataset can be used to develop and evaluate machine/deep learning models for change detection, semantic segmentation and landslide extraction. Paper: Zhang et al., 2023
2023.EGY-BCD
The EGY-BCD dataset is designed to detect building changes from high-resolution satellite imagery with a resolution of 0.25 m/pixel (level 19). The dataset includes four urban and coastal areas in Egypt, collected from Google Earth over two different periods between 2015 and 2022. The dataset contains 6091 pairs of small images of size 256×256 and is randomly divided into three sets: a training set (70%), a validation set (20%), and a test set (10%). The ground-truth data for each pair of images is labeled into two categories, "no-change" or "change," to train the proposed network. Paper: Holail et al.2023
2023.SI-BU dataset
The SI-BU dataset comprises post-phase satellite imagery captured from Google Earth (Google Inc.) in 2021 of Guiyang, Guizhou province, China, along with corresponding labels. The dataset covers an area of approximately 172 km2 and contains buildings of varying height, scale, and appearance. The images and labels were cropped into non-overlapping pairs of 512 × 512 pixels, with 3,604 pairs for training and 1,328 pairs for testing. The labels indicate four categories: background, unchanged buildings, newly constructed buildings, and removed buildings, which are assigned values of 0, 1, 2, and 3, respectively. The labels were meticulously annotated by image interpretation experts to indicate changes between the images and building masks collected from the same location in 2019. The dataset exhibits an off-nadir problem, particularly for high-rise buildings, due to the offset between building rooftops and footprints, which makes it challenging to automatically extract building changes from the dataset. Paper: Liao et al.2023
2023.CNAM-CD
CNAM-CD is a multi-class change detection dataset that collects images of 12 different urban scenes from the past decade. The dataset selects 12 State-level New Areas in China as the study area and contains 2503 pairs of GeoTiff format images with a pixel size of 512×512. The images were captured at different times from 2013 to 2022. The data source is Google Earth, and the resolution is 0.5m. Paper: Zhou et al.2023
2023.BANDON (Building Change Detection with Off-nadir Aerial Images Dataset)
The BANDON dataset is designed for building change detection using off-nadir aerial images. It consists of 2283 image pairs from urban and rural areas with corresponding change, BT-flows, segmentation, and ST-offsets labels (test sets don't have auxiliary annotations). BANDON provides novel data for the off-nadir building change detection task, and its detailed annotations support multi-task learning in aerial images. Paper: Pang et al.2023
2022.LEVIR Change Captioning (LEVIR-CC) dataset
LEVIR-CC dataset contains 10077 pairs of bitemporal RS images and 50385 sentences describing the differences between images. The images of the LEVIR-CC dataset are mainly from the CD dataset LEVIR-CD. LEVIR-CC dataset may help explore models to align visual changes and language in RS images. Paper: Liu et al.2022
2022.DynamicEarthNet
The DynamicEarthNet dataset includes daily satellite data from January 2018 to December 2019, covering 75 areas of interest around the world with diverse land cover changes. It provides a sequence of daily revisited images for each region, as well as pixel-wise semantic labels for the first day of each month at a resolution of 1024x1024 and pixel granularity of 3 meters, which serve as ground-truth for defining land cover changes over the two-year period. Paper: Toker A et al.2022
2022.Multisource built-up change (MSBC) and multisource OSCD (MSOSCD) datasets
The datasets are made to fill the gap of built-up CD datasets including multispectral, SAR, and VHR. MSBC is labeled based on GF-2 VHR images, and the MSOSCD is reformed from an existing dataset—Onera Satellite CD(OSCD) dataset. Paper: Li et al.2022
2022.CLCD dataset
The CLCD dataset consists of 600 pairs image of cropland change samples, with 320 pairs for training, 120 pairs for validation and 120 pairs for testing. The bi-temporal images in CLCD were collected by Gaofen-2 in Guangdong Province, China, in 2017 and 2019, respectively, with spatial resolution ranged from 0.5 to 2 m. Each group of samples is composed of two images of 512 × 512 and a corresponding binary label of cropland change. The main types of change annotated in CLCD include buildings, roads, lakes and bare soil lands, etc. Paper: Liu et al.2022
2021.QFabric
QFabric is a comprehensive temporal multi-task dataset with 450,000 change polygons across 504 locations in 100 cities, using imagery from Maxar’s WorldView-2 Satellite collected between January 2014 and July 2020. It includes 6 change types and 9 change status classes, and the accompanying geography and environment metadata provides valuable context for deep neural network development. Paper: Verma S et al.2021
2021.HTCD dataset
The HTCD dataset, a new Satellite-UAV heterogeneous image data set, was built using the satellite images from Google Earth and UAV images from Open Aerial Map. The size of the satellite image is 11 K×15 K pixels. While the UAV image is consisted of 15 image blocks, in total 1.38 M×1.04 M pixels. The ground resolutions of them are 0.5971 m and 7.465 cm, respectively. Images and labels are all stored in GeoTiff format with location information, for the convenience of further analysis and research. Paper: Shao et al.2021
2021.Multi-modal Supervised Change Detection Data
Sentinel-1 SAR data were provided on the basis of OSCD dataset for multimodal supervised change detection (SAR-SAR CD or Optical-SAR multi-modal CD). Paper: Ebel et al.2021
2021.S2Looking
S2Looking, a building change detection dataset that contains large-scale side-looking satellite images captured at varying off-nadir angles. It consists of 5000 registered bitemporal image pairs (size of 1024*1024, 0.5 ~ 0.8 m/pixel) of rural areas throughout the world and more than 65,920 annotated change instances. It provides two label maps to separately indicate the newly built and demolished building regions for each sample in the dataset. Paper: Shen et al.2021
2021.SYSU-CD
The dataset contains 20000 pairs of 0.5-m aerial images of size 256×256 taken between the years 2007 and 2014 in Hong Kong.The main types of changes in the dataset include: (a) newly built urban buildings; (b) suburban dilation; (c) groundwork before construction; (d) change of vegetation; (e) road expansion; (f) sea construction. Paper: Shi et al.2021
2021.Sentinel-2 Multitemporal Cities Pairs (S2MTCP) dataset
The S2MTCP dataset contains N = 1520 image pairs, spread over all inhabited continents, with the highest concentration of image pairs in North-America, Europe, and Asia. Bands with a spatial resolution smaller than 10 m are resampled to 10 m and images are cropped to approximately 600x600 pixels. It was created for self-supervised training. Paper: Leenstra et al.2021
2020.Hi-UCD
Hi-UCD focuses on urban changes and uses ultra-high resolution images to construct multi-temporal semantic changes to achieve refined change detection. The study area of Hi-UCD is a part of Tallinn, the capital of Estonia, with an area of 30km2. There are 359 image pairs in 2017-2018, 386 pairs in 2018-2019, and 548 pairs in 2017-2019, including images, semantic maps, and change maps at different times. Each image has a size of 1024 x 1024 and a spatial resolution of 0.1 m. There are 9 types of objects, including natural objects (water, grassland, woodland,