Repositorio Institucional de la Universidad de Burgos (RIUBU)
metadata only
Dilated residual U-Net with spatial–channel attention for building footprint extraction from UAV imagery in diverse landscapes
Accurate extraction of building footprints from unmanned aerial vehicle (UAV) imagery is essential for urban planning, infrastructure monitoring, and disaster risk management. However, segmentation performance remains challenged by scale variability, spectral ambiguity, and partial occlusion in h
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Advancing Point Source Methane Emissions Quantification With UAV Sampling and Deep Learning: A Large‐Eddy Simulation Study#147563
- Post-Wildfire Erosion and Biogeochemistry: Integrating Aerial Imagery and Soil Testing to Assess Landscape Recovery#176222
- Landscape: Window Overlooking the Woods#498535
- GeoWind2Plan: Mission-Time 3D Urban Wind Prediction for Energy-Efficient UAV Planning#1130898
- Mapping Lupinus polyphyllus density and distribution along road verges using unmanned aerial vehicle (UAV)-based remote sensing.#685617
Record · ID 1040858
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive