Change detection for remote sensing images

Source: CHANGE DETECTION IN REMOTE SENSING IMAGES USING CONDITIONAL ADVERSARIAL NETWORKS

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 random selection of image pairs, the SECOND reflects real distributions of land-cover categories when changes occur.

For change detection tasks, current open-source datasets mainly focus on building extraction (e.g., WHU building dataset and LEVIR-CD dataset) (Chen and Shi, 2020; Ji et al., 2018) and urban development monitoring (e.g., SECOND dataset, Google dataset and CDD dataset) (Yang et al., 2022; Peng et al., 2021; Lebedev et al., 2018), whereas datasets for natural disaster monitoring have been seldom investigated. Therefore, we sought to present the GVLM dataset, 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 total coverage of the dataset is $163.77 km2$. 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.

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. The dataset is split into: - train set - validation set - hidden test set, for which ground truth labels are not disclosed. The hdf5 file is structured in this way: root | |- uuid_0: {"post_fire", "pre_fire", "mask"} | | |- uuid_1: {"post_fire", "pre_fire", "mask"} | ... Each uuid have associated an attribute called fold. Dataset can be downloaded from here. You can find a script that can be used to load data. Additional optional data are available here. If you need more data, contact us.