Monitoring construction changes using dense satellite time series and deep learning

JW Suh, Z Zhu, Y Zhao - Remote Sensing of Environment, 2024‏ - Elsevier
Monitoring construction changes is essential for understanding the anthropogenic impacts
on the environment. However, map** construction changes at a medium scale (ie, 30 m) …

Map** crop** intensity by identifying bare soil occurrence from Sentinel-2 time series

Y Huang, S Ye, J Xue, Z Shi, F Wang - Computers and Electronics in …, 2024‏ - Elsevier
Timely and accurate information on field management practices is of critical importance to
agricultural production, food security and ecological protection. The temporal changes of …

A multi-source change detection algorithm supporting user customization and near real-time deforestation detections

IR McGregor, G Connette, JM Gray - Remote Sensing of Environment, 2024‏ - Elsevier
The abundance of free and accessible satellite data has revolutionized our ability to study
deforestation with remote sensing. Recent advances have enabled us to monitor …

A near-real-time tropical deforestation monitoring algorithm based on the CuSum change detection method

B Ygorra, F Frappart, JP Wigneron, T Catry… - Frontiers in Remote …, 2024‏ - frontiersin.org
Tropical forests are currently under pressure from increasing threats. These threats are
mostly related to human activities. Earth observations (EO) are increasingly used for …

BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response

H Chen, J Song, O Dietrich, C Broni-Bediako… - arxiv preprint arxiv …, 2025‏ - arxiv.org
Disaster events occur around the world and cause significant damage to human life and
property. Earth observation (EO) data enables rapid and comprehensive building damage …

Characterizing and Detecting Multiscenario Degradation of the Maidika Alpine Wetland Nature Reserve in the Qinghai–Tibet Plateau Using Landsat Time Series

Y Chen, R Ci, D Zhong, L Liu, J Yu… - Journal of Remote …, 2025‏ - spj.science.org
Monitoring alpine wetland degradation on the Qinghai–Tibet Plateau is crucial for
understanding the responses to and resilience against climate change but has been …

[PDF][PDF] Exposure to deforestation: is statistical inference robust to choices in land cover modelling?

M Weidinger, S Nichols, S Dietrich - 2024‏ - mathiasweidinger.com
Earth observation data has greatly enriched social science research, especially in contexts
where data is otherwise scarce or likely to suffer from measurement error. However, social …