S. Molina J. Mothe – Page 2 – AI4AGRI – Romanian Excellence Center on AI for Agriculture

Training material for wheat and lucerne detection in a PRISMA hyperspectral image

https://colab.research.google.com/drive/1n_LmmqkwC-SPNALjUA7Wyx9XNi2XiY3v#scrollTo=yGjBSDSkECrl This was delivered at the International Summer School on Advanced Remote Sensing, 7 – 12 October 2024, Faculty of Land Reclamation and Environmental Engineering, University of Agronomic Sciences and Veterinary Medicine of Bucharest. https://fifim.ro/international-summer-school-on-advanced-remote-sensing There was a total of 41 participants.

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Training material for Hyperspectral Image Visualization

Training material for Hyperspectral Image Visualization Read More »

DACIA5 – A Sentinel 2-based Multispectral Dataset for Agricultural Crop Identification Applications over Brasov area, Romania

Datasets / S. Molina J. Mothe

As part of the AI4AGRI project, a new dataset is now available that combines Sentinel-1 SAR and Sentinel-2 multispectral imagery to support research in crop identification using machine learning and remote sensing. Covering the years 2020 to 2024 over an agricultural area north of Brașov, Romania, this dataset is structured to address two key tasks:

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Invitation to Webinar on Hyperspectral Remote Sensing – 19 February 2025, online

Events, Past Events / S. Molina J. Mothe

📅 Wed. 19 February 2025

Invitation to Webinar on Hyperspectral Remote Sensing – 19 February 2025, online Read More »

Water Challenges in Occitania – Webinar Series

Events, Past Events / S. Molina J. Mothe

📅 Every Month until February

Water Challenges in Occitania – Webinar Series Read More »

A Review of CNN Applications in Smart Agriculture Using Multimodal Data

AI4AGRI Publications, Publications / S. Molina J. Mothe

📅 15 January 2025
🧑 Mohammad El Sakka | Mihai Ivanovici | Loft Chaari | Josiane Mothe

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muDACIA5: Sentinel-2 Brasov area 2020-2024 multi-spectral dataset for crop monitoring and identification

Datasets / S. Molina J. Mothe

As part of the AI4AGRI project, a new dataset is now available for researchers focusing on crop identification using remote sensing and machine learning techniques. The dataset consists of Sentinel-2 MSI images acquired between 2020 and 2024 over an area north of Brașov, Romania. It is designed to support two specific tasks: The dataset includes:

muDACIA5: Sentinel-2 Brasov area 2020-2024 multi-spectral dataset for crop monitoring and identification Read More »

Author name: S. Molina J. Mothe