Geospatial Computer Vision has become one of the most prevalent emerging fields of investigation in Earth Observation in the last few years. In this theme issue, we aim at showcasing a number of works at the interface between remote sensing, photogrammetry, image processing, computer vision and machine learning. In light of recent sensor developments - both from the ground as from above - an unprecedented (and ever growing) quantity of geospatial data is available for tackling challenging and urgent tasks such as environmental monitoring (deforestation, carbon sequestration, climate change mitigation), disaster management, autonomous driving or the monitoring of conflicts. The new bottleneck for serving these applications is the extraction of relevant information from such large amounts of multimodal data. This includes sources, stemming from multiple sensors, that exhibit distinct physical nature of heterogeneous quality, spatial, spectral and temporal resolutions. They are as diverse as multi-/hyperspectral satellite sensors, color cameras on drones, laser scanning devices, existing open land-cover geodatabases and social media. Such core data processing is mandatory so as to generate semantic land-cover maps, accurate detection and trajectories of objects of interest, as well as by-products of superior added-value: georeferenced data, images with enhanced geometric and radiometric qualities, or Digital Surface and Elevation Models.
|Number of pages||2|
|Journal||ISPRS journal of photogrammetry and remote sensing|
|Early online date||9 Jan 2018|
|Publication status||Published - Jun 2018|