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Found 9 papers, 2 papers with code
A CNN regression model to estimate buildings height maps using Sentinel-1 SAR and Sentinel-2 MSI time series
Accurate estimation of building heights is essential for urban planning, infrastructure management, and environmental analysis.
Context-Aware Change Detection With Semi-Supervised Learning
Change detection using earth observation data plays a vital role in quantifying the impact of disasters in affected areas.
Multi-Modal Deep Learning for Multi-Temporal Urban Mapping With a Partly Missing Optical Modality
In addition, a reconstruction network is utilized to approximate the optical features based on the SAR data in case of a missing optical modality.
Investigating Imbalances Between SAR and Optical Utilization for Multi-Modal Urban Mapping
In this paper, we investigate the utilization of SAR and optical data for urban mapping.
Mapping Urban Population Growth from Sentinel-2 MSI and Census Data Using Deep Learning: A Case Study in Kigali, Rwanda
Since Post-Classification Comparison (PCC) methods for change detection are known to propagate the error of the individual maps, we propose an end-to-end population growth mapping method.
Unsupervised Flood Detection on SAR Time Series
Our proposed CD model is evaluated on flood detection data.
Urban Change Detection Using a Dual-Task Siamese Network and Semi-Supervised Learning
Second, SSL was adopted to improve supervised change detection.
Building Change Detection using Multi-Temporal Airborne LiDAR Data
For the change detection task, we used multi-temporal airborne LiDAR data.
Attentive Dual Stream Siamese U-net for Flood Detection on Multi-temporal Sentinel-1 Data
The experiments highlight that the combination of bi-temporal SAR data with an effective network architecture achieves more accurate flood detection than uni-temporal methods.
