Impact of digital twins and metaverse on cities: History, current situation, and application perspectives
To promote the expansion and adoption of Digital Twins (DTs) in Smart Cities (SCs), a
detailed review of the impact of DTs and digitalization on cities is made to assess the …
detailed review of the impact of DTs and digitalization on cities is made to assess the …
AI security for geoscience and remote sensing: Challenges and future trends
Recent advances in artificial intelligence (AI) have significantly intensified research in the
geoscience and remote sensing (RS) field. AI algorithms, especially deep learning-based …
geoscience and remote sensing (RS) field. AI algorithms, especially deep learning-based …
Using machine learning and remote sensing to track land use/land cover changes due to armed conflict
Armed conflicts have detrimental impacts on the environment, including land systems. The
prevailing understanding of the relation between Land Use/Land Cover (LULC) and armed …
prevailing understanding of the relation between Land Use/Land Cover (LULC) and armed …
[HTML][HTML] Instance segmentation of standing dead trees in dense forest from aerial imagery using deep learning
Mapping standing dead trees, especially, in natural forests is very important for evaluation of
the forest's health status, and its capability for storing Carbon, and the conservation of …
the forest's health status, and its capability for storing Carbon, and the conservation of …
[HTML][HTML] Spatially transferable dwelling extraction from Multi-Sensor imagery in IDP/Refugee Settlements: A meta-Learning approach
Dwelling information is very important for various applications in humanitarian emergency
response. For this, Earth observation is crucial to have spatially explicit and temporally …
response. For this, Earth observation is crucial to have spatially explicit and temporally …
Comparing OBIA-Generated Labels and Manually Annotated Labels for Semantic Segmentation in Extracting Refugee-Dwelling Footprints
Refugee-dwelling footprints derived from satellite imagery are beneficial for humanitarian
operations. Recently, deep learning approaches have attracted much attention in this …
operations. Recently, deep learning approaches have attracted much attention in this …
[HTML][HTML] Automatic impervious surface mapping in subtropical China via a terrain-guided gated fusion network
Z Li, A Zhang, G Sun, Z Han, X Jia - International Journal of Applied Earth …, 2024 - Elsevier
Large-scale, high-resolution and multi-temporal impervious surface maps from remote
sensing are essential for socioeconomic and environmental studies. However, in complex …
sensing are essential for socioeconomic and environmental studies. However, in complex …
Mapping Dwellings in IDP/Refugee Settlements Using Deep Learning
The improvement in computer vision, sensor quality, and remote sensing data availability
makes satellite imagery increasingly useful for studying human settlements. Several …
makes satellite imagery increasingly useful for studying human settlements. Several …
Using spatial video and deep learning for automated mapping of ground-level context in relief camps
Background The creation of relief camps following a disaster, conflict or other form of
externality often generates additional health problems. The density of people in a highly …
externality often generates additional health problems. The density of people in a highly …
Unsupervised Domain Adaptation for Instance Segmentation: Extracting Dwellings in Temporary Settlements Across Various Geographical Settings
GW Gella, C Pelletier, S Lefèvre… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Dwelling information is essential for humanitarian emergency response during or in the
aftermath of disasters, especially in temporary settlement areas hosting forcibly displaced …
aftermath of disasters, especially in temporary settlement areas hosting forcibly displaced …