SUNScholar
metadata only
Crop type mapping using LiDAR, Sentinel-2 and aerial imagery with machine learning algorithms
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
- Remote-Sensing City Layout Extraction with MLLM#625072
- Remote-Sensing City Layout Extraction with MLLM#622799
- FLORA: A deep learning approach to predict forest attributes from heterogeneous LiDAR data#324921
- Edge-IoT-UAV adaptation toward precision agriculture using 3D-LiDAR point clouds#203616
- Mapping seasonal dynamics of forage and cereal crops in a hyper-arid environment using Sentinel-1 and Sentinel-2 time series.#439027
Record · ID 437858
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