Awesome Remote Sensing Change Detection

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

1 Dateset

1.1 Multispectral

With label

• 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, bare land), artificial objects (Building,greenhouse, road, bridge), and others (change-related), basically include all types of urbanland cover in Estonia. Paper: Tian et al.2020

• 2020.SEmantic Change detectiON Dataset (SECOND)
  SECOND, a well-annotated semantic change detection dataset, which collects 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. SECOND 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. Paper: Yang et al.2020

• 2020.Google Dataset
  The images were acquired during the periods between 2006 and 2019, covering the suburb areas of Guangzhou City, China. To facilitate image pair generation, Google Earth service through the BIGEMAP software was adopted to collect 19 season-varying VHR images pairs with three bands of red, green, and blue, a spatial resolution of 0.55 m, and the size ranging from 1006×1168 pixels to 4936×5224 pixels. The annotation is focused on buildings. Paper: Peng et al.2020

• 2020.LEVIR building Change Detection (LEVIR-CD) Dataset
  LEVIR-CD consists of 637 very high-resolution (VHR, 0.5m/pixel) Google Earth image patch pairs with a size of 1024 × 1024 pixels. These bitemporal images with time span of 5 to 14 years have significant land-use changes, especially the construction growth. LEVIR-CD covers various types of buildings, such as villa residences, tall apartments, small garages and large warehouses. The fully annotated LEVIR-CD contains a total of 31,333 individual change building instances. Paper: Chen et al.2020

• 2019.High Resolution Semantic Change Detection (HRSCD) Dataset
  This dataset contains 291 coregistered image pairs of RGB aerial images from IGS's BD ORTHO database. Pixel-level change and land cover annotations are provided, generated by rasterizing Urban Atlas 2006, Urban Atlas 2012, and Urban Atlas Change 2006-2012 maps. Paper: Daudt et al.2019

• 2019.Wuhan multi-temperature scene (MtS-WH) Dataset
  The dataset is mainly used for theoretical research and verification of scene change detection methods. It consists of two large-size VHR images, which have a size of 7200x6000 and are respectively acquired by IKONOS sensors in Feb 2002 and Jun 2009. The images cover the Hanyang District, Wuhan City, China and contain 4 spectral bands (Blue, Green, Red, and Near-Infrared). The spatial resolution of the images is 1m after fusion of the pan and multispectral images by the Gram–Schmidt algorithm. For a discussion of this dataset, please refer to #3. Paper: Wu et al.2017

• 2018.WHU Building change detection Dataset
  The dataset contains two aerial images (0.2m/pixel, 15354×32507) and provides a change vector, a change raster map, and two corresponding building vectors of these two aerial images. Paper: Ji et al.2018

• 2018.Synthetic images and real season-varying remote sensing images
  This dataset has three types: synthetic images without objects relative shift, synthetic images with small relative shift of objects, real season-varying remote sensing images (obtained by Google Earth). The real season-varying remote sensing images have 16000 image sets with image size 256x256 pixels (10000 train sets and 3000 test and validation sets) and a spatial resolution of 3 to 100 cm/px. For a discussion of this dataset, please refer to #5. Paper: Lebedev et al.2018

• 2018.Onera Satellite Change Detection Dataset
  This dataset addresses the issue of detecting changes between satellite images from different dates. It comprises 24 pairs of multispectral images taken from the Sentinel-2 satellites between 2015 and 2018. Locations are picked all over the world, in Brazil, USA, Europe, Middle-East and Asia. For each location, registered pairs of 13-band multispectral satellite images obtained by the Sentinel-2 satellites are provided. Images vary in spatial resolution between 10m, 20m, and 60m. Paper: Daudt et al.2018

• 2011.The Aerial Imagery Change Detection (AICD) dataset
  This dataset contains synthetic aerial images with artificial changes generated with a rendering engine. It contains 1000 pairs of 800x600 images, each pair consisting of one reference image and one test image, and the 1000 corresponding 800x600 ground truth masks. Paper: Bourdis et al.2011

• 2008.SZTAKI AirChange Benchmark set
  This dataset contains 13 aerial image pairs of size 952x640 and resolution 1.5m/pixel and binary change masks (drawn by hand). Each record contains a pair of preliminary registered input images and a mask of the 'relevant' changes. The input images are taken with 5, 7 resp. 23 years of time differences. During the generation of the change mask, we have considered the following differences as relevant changes: (a) new built-up regions (b) building operations (c) planting of large group of trees (d) fresh plow-land (e) groundwork before building over. Note that the ground truth does NOT contain change classification, only binary change-no change decision for each pixel. Paper: Benedek et al.2008

Without label

• Planet Disaster Datasets
  Planet will make PlanetScope imagery available to the public during a select disaster event.

• DigitalGlobe's Open Data Program
  DigitalGlobe will release open imagery (worldview-3 or other) for select sudden onset major crisis events, including pre-event imagery, post-event imagery, and a crowdsourced damage assessment.

• French National Institute of Geographical and Forest Information (IGN),BD ORTHO
  The datasets are mosaics of aerial images taken by the French National Institute of Geographical and Forest Information (IGN). They come from a database named BD ORTHO which contains orthorectified aerial images of several regions of France from different years at a resolution of 20 cm or 50 cm per pixel.

• LINZ DATA SERVICE
  The New Zealand Land Information Services website provides multi-temporal aerial images of some New Zealand cities, all of which have a resolution of over 1m.

• USGS EarthExplorer
  An epic level database, which can provide multi-temporal，multi-sensor and multi-resolution data.

1.2 Hyperspectral

• 2018.Hyperspectral Change Detection Dataset
  This dataset can be used to perform change detection techniques in multi-temporal hyperspectral images. It includes two different hyperspectral scenes from the AVIRIS sensor: The Santa Barbara scene, taken on the years 2013 and 2014 with the AVIRIS sensor over the Santa Barbara region (California) whose spatial dimensions are 984 x 740 pixels and includes 224 spectral bands. The Bay Area scene, taken on the years 2013 and 2015 with the AVIRIS sensor surrounding the city of Patterson (California) whose spatial dimensions are 600 x 500 pixels and includes 224 spectral bands. It also includes a hyperspectral scene from the HYPERION sensor: The Hermiston city scene, taken on the years 2004 and 2007 with the HYPERION sensor over the Hermiston City area (Oregon) whose spatial dimensions are 390 x 200 pixels and includes 242 spectral bands. 5 types of changes related to crop transitions are identified in this scene. Paper: López-Fandiño et al.2018

• 2018.GETNET
  This dataset has two hyperspectral images, which were acquired on May 3, 2013, and December 31, 2013, respectively in Jiangsu province, China. It has a size of 463×241 pixels, with 198 bands available after noisy band removal. In the ground-truth map, white pixels represent changed portions and black pixels mean unchanged parts. Paper: Wang et al.2018

2 Code

2.1 Multispectral

2.1.1 Traditional Method

• 2019.Bobholamovic/ChangeDetectionToolbox

• 2019.Canty M J. Image Analysis, Classification and Change Detection in Remote Sensing (Fourth Revised Edition)

• 2017.Canty M J. Change Detection with Google Earth Engine Imagery

• 2014.Canty M J. Image Analysis, Classification and Change Detection in Remote Sensing (Third Revised Edition)

• Zhu Z, Woodcock C E, et al. Algorithm developed for Continuous Change Detection and Classification (CCDC) of land cover using all available Landsat data

• Implementation of " 2009.Celik T. Unsupervised change detection in satellite images using principal component analysis and k-means clustering ".
  Matlab, Python

• 2007.Nielsen A A.The Regularized Iteratively Reweighted Multivariate Alteration Detection (IR-MAD)

2.1.2 Deep Learning

• 2021.Fang S, Li K, Shao J, et al. SNUNet-CD: A Densely Connected Siamese Network for Change Detection of VHR Images

• 2020.I-Hope-Peace/ChangeDetectionRepository

• 2020.Diakogiannis F I, Waldner F, Caccetta P. Looking for change? Roll the Dice and demand Attention

• 2020.Zhang C, Yue P, Tapete D, et al. A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images

• 2020.Chen H, Shi Z. A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection

• 2020.Zhang M, Shi W. A Feature Difference Convolutional Neural Network-Based Change Detection Method

• 2020.llu025/Heterogeneous_CD

• 2020.neverstoplearn/remote_sensing_change_detection

• 2020.Chen J, Yuan Z, Peng J, et al. DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images

• 2019.Saha S et al. Unsupervised deep change vector analysis for multiple-change detection in VHR images

• 2019.Papadomanolaki M, Verma S, Vakalopoulou M, et al. Detecting Urban Changes with Recurrent Neural Networks from Multitemporal Sentinel-2 Data

• 2019.Du B, Ru L, Wu C, et al. Deep Slow Feature Analysis Network (DSFANet)

• 2019.Peng D, Zhang Y, Guan H. End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++

• 2018.Rahman F, Vasu B, Van Cor J, et al. Siamese Network with Multi-level Features for Patch-based Change Detection in Satellite Imagery

• 2018.Daudt R C, Le Saux B, Boulch A. Fully convolutional siamese networks for change detection

• 2018.Guo E, Fu X, Zhu J, et al. Learning to measure change: Fully convolutional siamese metric networks for scene change detection

• 2018.Maiya S R, Babu S C. Slum segmentation and change detection: A deep learning approach

• 2016.El Amin A M, Liu Q, Wang Y. Convolutional neural network features based change detection in satellite images

2.2 SAR

• 2019.Gao F, Wang X, Gao Y, et al. Sea ice change detection in SAR images based on convolutional-wavelet neural networks (Matlab)

• 2019.Gao Y, Gao F, Dong J, et al. Transferred deep learning for sea ice change detection from synthetic-aperture radar images (Matlab)

• 2015.Conradsen K, Nielsen A A, Skriver H, et al. Change detection in polarimetric SAR Data (Python)

3.Contest

• SpaceNet 7: Multi-Temporal Urban Development Challenge (CosmiQ Works, Planet, Aug 2020) In this challenge, participants will identify and track buildings in satellite imagery time series collected over rapidly urbanizing areas. The competition centers around a new open source dataset of Planet satellite imagery mosaics, which will include 24 images (one per month) covering ~100 unique geographies. The dataset will comprise 40,000 km2 of imagery and exhaustive polygon labels of building footprints in the imagery, totaling over 3M individual annotations. Challenge participants will be asked to track building construction over time, thereby directly assessing urbanization.

• 商汤科技首届AI遥感解译大赛-变化检测赛道 (SenseTime, Aug 2020) 数据集：4662组，分辨率：0.5~3m，规格：512*512，变化类型为6种主要土地性质之间的相互转化：水体、地面、低矮植被、树木、建筑物、运动场。每组数据中，前后时相的两张图片各自对应一张标注图，表示发生变化的区域以及该图片变化区域内各时期的土地性质。第一名的解决方案请详见 LiheYoung/SenseEarth2020-ChangeDetection。

• xView 2 Building Damage Asessment Challenge (DIUx, Nov 2019)
  550k building footprints & 4 damage scale categories, 20 global locations and 7 disaster types (wildfire, landslides, dam collapses, volcanic eruptions, earthquakes/tsunamis, wind, flooding), Worldview-3 imagery (0.3m res.), pre-trained baseline model. Paper: Gupta et al. 2019

• 遥感图像稀疏表征与智能分析竞赛-变化检测赛道 (Wuhan University,et al. Jul 2019) 本项竞赛以光学遥感图像为处理对象，参赛队伍使用主办方提供的遥感图像进行建筑物变化检测，主办方根据评分标准对变化检测结果进行综合评价。竞赛中将提供两个不同时间获取的大尺度高分辨率遥感图像（包含蓝、绿、红和近红外四个波段），以及图像中变化区域的二值化标注数据集。

• 广东政务数据创新大赛—智能算法赛 (Alibaba,et al. Nov 2017) 使用2015年和2017年分别获取到的广东省某地的Quickbird卫星影像（包含蓝、绿、红和近红外四个波段），识别出两年之间新增的人工地上建筑物（不包括道路）。获胜团队的解决方案可以在天池官网上找到。

Reference

• chrieke/awesome-satellite-imagery-datasets
• Zhang Bin's Blog. Remote Sensing Datasets
• MinZHANG-WHU/Change-Detection-Review: This may be the most comprehensive review of change detection in github to date.
• The picture of this page is from Mou L et al. 2018