Remote-sensing Libraries

Extracting roads is challenging due to the occlusions caused by other objects and the complex traffic environment In instance segmentation, each individual 'instance' of a segmented area is given a u…

Change detection based on remote sensing (RS) data is an important method of detecting changes on the Earth’s surface and has a wide range of applications in urban planning, environmental monitoring,…

Be aware that the project is new and is evolving to maturity with CNES usage roadmaps and projects such as: CARS is available on Docker Hub and can be downloaded by: You only need to launch one comma…

Additionally, it also provides multiple methods for computing land surface emissivity. It is targeted towards supporting research and science workflows in many fields including climate science, earth…

When geospatial raster data is concerned in a machine learning pipeline, it is often required to extract meaningful features, such as vegetation indices (e.g., NDVI, EVI, NDRE, etc.) or textures. Thi…

The core algorithms responsible for calculating river centerlines and widths are identical in the JavaScript and the Python version. However, there is minor differences in how users might call these …

Source files for the Docker image mort/crc4docker Python scripts for the textbook "Image Analysis, Classification and Change Detection in Remote Sensing, Fourth Revised Edition" On Ubuntu, for exampl…

With the rapid development of Earth observation technology, very-high-resolution (VHR) images from various satellite sensors are more available, which greatly enrich the data source of change detecti…

With the development of Earth observation technology, very-high-resolution (VHR) image has become an important data source of change detection. Nowadays, deep learning methods have achieved conspicuo…

Satellite imagery has become a primary data source in the natural sciences, economics, archaeology, sustainability, national security & defence, and many other domains which utilize geospatial in…