The dataset consists of an aerial image sub-dataset, two satellite image sub-datasets and a building change detection sub-dataset covering more than 1400 km2.
We create an omnidirectional image dataset of real street scenes called OSV dataset with multi-class annotations for spherical object detection. It was collected by a vehicle-mounted panoramic camera and contains 1777 lights, 867 cars, 578 traffic signs, 867 crosswalks and 355 crosswalk warning lines, totally 5636 objects.
Multi-temporal multi-spectral GF2 satellite image dataset for crop type classification along with the 3D FCN with Global pooling module and Channel attention module (3DFGC) source code.
We create an open remote sensing landslide dataset called Bijie landslide dataset for developing automatic landslide detection methods. The dataset consists of satellite optical images, shapefiles of landslides’ boundaries and digital elevation models. All the images in this dataset, i.e. 770 landslide images (red points) and 2003 non-landslide images were cropped from the TripleSat satellite images captured from May to August 2018.
We manually edited a Landsat 8 dataset for cloud detection and removal, which contains the cloudy images, corresponding cloudless historical images, and cloud and shadow masks.
We created the synthetic aerial dataset for large-scale Earth surface reconstruction called the WHU dataset.
We introduce a second-order pooling named compact bilinear pooling (CBP) into convolutional neural networks (CNNs) for remote sensing image retrieval.
We develops a feature-based simultaneous localization and mapping (SLAM) system for panoramic image sequences obtained from a multiple fisheye camera rig in a wide baseline mobile mapping system (MMS).
We develop a deep learning-based matching method between an RGB and an infrared image that were captured from satellite sensors.
We propose a novel end-to-end GAN-based full-space domain adaptation learning framework.