Remote Sensing Image Classification

Million-AID is a large-scale benchmark dataset containing a million instances for RS scene classification. There are 51 semantic scene categories in Million-AID. And the scene categories are customized to match the land-use classification standards, which greatly enhance the practicability of the constructed Million-AID. Different form the existing scene classification datasets of which categories are organized with parallel or uncertain relationships, scene categories in Million-AID are organized with systematic relationship architecture, giving it superiority in management and scalability. Specifically, the scene categories in Million-AID are organized by the hierarchical category network of a three-level tree: 51 leaf nodes fall into 28 parent nodes at the second level which are grouped into 8 nodes at the first level, representing the 8 underlying scene categories of agriculture land, commercial land, industrial land, public service land, residential land, transportation land, unutilized land, and water area. The scene category network provides the dataset with excellent organization of relationship among different scene categories and also the property of scalability. The number of images in each scene category ranges from 2,000 to 45,000, endowing the dataset with the property of long tail distribution. Besides, Million-AID has superiorities over the existing scene classification datasets owing to its high spatial resolution, large scale, and global distribution. Source: DiRS: On Creating Benchmark Datasets for Remote Sensing Image Interpretation Image Source: https://captain-whu.github.io/DiRS/

AID is a new large-scale aerial image dataset, by collecting sample images from Google Earth imagery. Note that although the Google Earth images are post-processed using RGB renderings from the original optical aerial images, it has proven that there is no significant difference between the Google Earth images with the real optical aerial images even in the pixel-level land use/cover mapping. Thus, the Google Earth images can also be used as aerial images for evaluating scene classification algorithms. The new dataset is made up of the following 30 aerial scene types: airport, bare land, baseball field, beach, bridge, center, church, commercial, dense residential, desert, farmland, forest, industrial, meadow, medium residential, mountain, park, parking, playground, pond, port, railway station, resort, river, school, sparse residential, square, stadium, storage tanks and viaduct. All the images are labelled by the specialists in the field of remote sensing image interpretation, and some samples of each class are shown in Fig.1. In all, the AID dataset has a number of 10000 images within 30 classes. The images in AID are actually multi-source, as Google Earth images are from different remote imaging sensors. This brings more challenges for scene classification than the single source images like UC-Merced dataset. Moreover, all the sample images per each class in AID are carefully chosen from different countries and regions around the world, mainly in China, the United States, England, France, Italy, Japan, Germany, etc., and they are extracted at different time and seasons under different imaging conditions, which increases the intra-class diversities of the data.

WHU-RS19 is a set of satellite images exported from Google Earth, which provides high-resolution satellite images up to 0.5 m. Some samples of the database are displayed in the following picture. It contains 19 classes of meaningful scenes in high-resolution satellite imagery, including airport, beach, bridge, commercial, desert, farmland, footballfield, forest, industrial, meadow, mountain, park, parking, pond, port, railwaystation, residential, river, and viaduct. For each class, there are about 50 samples. It’s worth noticing that the image samples of the same class are collected from different regions in satellite images of different resolutions and then might have different scales, orientations and illuminations.

In this work, we propose a novel remote sensing dataset, FireRisk, consisting of 7 fire risk classes with a total of 91 872 labelled images for fire risk assessment. This remote sensing dataset is labelled with the fire risk classes supplied by the Wildfire Hazard Potential (WHP) raster dataset, and remote sensing images are collected using the National Agriculture Imagery Program (NAIP), a high-resolution remote sensing imagery program. On FireRisk, we present benchmark performance for supervised and self-supervised representations, with Masked Autoencoders (MAE) pre-trained on ImageNet1k achieving the highest classification accuracy, 65.29%.

A new benchmark dataset of webcam images, Photi-LakeIce, from multiple cameras and two different winters, along with pixel-wise ground truth annotations. Source: Lake Ice Monitoring with Webcams and Crowd-Sourced Images

The DRIFT dataset includes 25k image patches collected in five European countries sourced from aerial and nanosatellite image archives. Each image patch is associated with three target variables to predict: 1. Canopy height: average height value for pixels containing woody vegetation. 2. Tree count: number of overstory (visible from an overhead perspective) trees in the images. 3. Tree cover fraction: percentage of the image being covered by overstory tree crowns. The DRIFT dataset includes significant shifts between label and visual distributions due to sensor and area differences. Furthermore, vegetation tends to grow to fit the local climate, therefore introducing concept drift in the data: same tree species may appear differently in different subsets. The label distribution also varies among different subsets (countries). The dataset is a good choice for: * image-level regression * domain adaption for regression * remote sensing for forest applications

The GDIT Aerial Airport dataset consists of aerial images containing instances of parked airplanes. All plane types have been grouped into a single classification named "airplane".

The HRPlanesv2 dataset contains 2120 VHR Google Earth images. To further improve experiment results, images of airports from many different regions with various uses (civil/military/joint) selected and labeled. A total of 14,335 aircrafts have been labelled. Each image is stored as a ".jpg" file of size 4800 x 2703 pixels and each label is stored as YOLO ".txt" format. Dataset has been split in three parts as 70% train, %20 validation and test. The aircrafts in the images in the train and validation datasets have a percentage of 80 or more in size. Link: https://github.com/dilsadunsal/HRPlanesv2-Data-Set

A high-resolution multi-sensor remote sensing scene classification dataset, appropriate for training and evaluating image classification models in the remote sensing domain. The dataset consists of 8400 overhead scenes, each covered by Airbus Pléiades, Airbus SPOT, and USDA NAIP imagery. The scenes are classified into 12 OpenStreetMap categories: * man_made=bridge * man_made=breakwater * building=farm * power=substation * leisure=stadium * leisure=golf_course * waterway=dam * landuse=quarry * landuse=farmland * landuse=forest * natural=water * natural=bare_rock

This dataset includes 3D point-cloud and 2D imagery from a flash LiDAR...

RTI International (RTI) generated 2,611 labeled point locations representing 19 different land cover types, clustered in 5 distinct agroecological zones within Rwanda. These land cover types were reduced to three crop types (Banana, Maize, and Legume), two additional non-crop land cover types (Forest and Structure), and a catch-all Other land cover type to provide training/evaluation data for a crop classification model. Each point is attributed with its latitude and longitude, the land cover type, and the degree of confidence the labeler had when classifying the point location. For each location there are also three corresponding image chips (4.5 m x 4.5 m in size) with the point id as part of the image name. Each image contains a P1, P2, or P3 designation in the name, indicating the time period. P1 corresponds to December 2018, P2 corresponds to January 2019, and P3 corresponds to February 2019. These data were used in the development of research documented in greater detail in “Deep Neural Networks and Transfer Learning for Food Crop Identification in UAV Images” (Chew et al., 2020).