Convolutional neural network (CNN)-based deep learning (DL) is a powerful, recently
developed image classification approach. With origins in the computer vision and image …

Convolutional neural network (CNN)-based deep learning (DL) has a wide variety of
applications in the geospatial and remote sensing (RS) sciences, and consequently has …

Semantic segmentation from very fine resolution (VFR) urban scene images plays a
significant role in several application scenarios including autonomous driving, land cover …

Land-surface parameters derived from digital land surface models (DLSMs)(for example,
slope, surface curvature, topographic position, topographic roughness, aspect, heat load …

Deep learning is a sub-field of artificial intelligence that combines feature engineering and
classification in one method. It is a data-driven technique that optimises a predictive model …

The past decade has witnessed the rapid development of geospatial artificial intelligence
(GeoAI) primarily due to the ground-breaking achievements in deep learning and machine …

Semantic segmentation is a fundamental but challenging problem of pixel-level remote
sensing (RS) data analysis. Semantic segmentation tasks based on aerial and satellite …

Spatially explicit, fine-grained datasets describing historical urban extents are rarely
available prior to the era of operational remote sensing. However, such data are necessary …

Remote sensing semantic segmentation plays a significant role in various applications such
as environmental monitoring, land use planning, and disaster response. Convolutional …

Topographical maps from the nineteenth century hold significant historical and
environmental value, providing insights into landscape changes over the past two centuries …