DIALNET OAI Articles
metadata only
Clasificación de tipos de vegetación con Inteligencia Artificial en datos satelitales en El Salvador
This study implements a vegetation classification process in El Salvador using remote sensing techniques and machine learning algorithms. Satellite data from Santa María, Usulután, were analyzed using convolutional neural networks (U-Net) and the Random Forest model. The objective is to identify and
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
- Data-driven interpretability of urban tree species classification from deep learning models and Satellite Image Time Series#161378
- Modelado predictivo de la ocurrencia de focos de incendios forestales en Colombia mediante técnicas de machine learning#327758
- Multi-sensor satellite data fusion and machine learning for Eucalyptus mapping in Meket district, Ethiopia.#781424
- Exploring Interpretability in Deep Learning-Based Forest Classification - Separating Similar Forest Classes using Sentinel-2 Time Series#1345093
- A Multi-Sensor Machine Learning Framework Integrating UAV Multispectral Imagery and LiDAR Data for Living Biomass Carbon Stock Estimation in Silviculturally Treated Forests#651903
Record · ID 1336611
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