Open to Collaborations

AI That Sees
Crops From Space

I'm Mohamed El Asri — a PhD researcher building hybrid CNN–Mamba models that classify crops from satellite radar across Morocco's farmlands. Making AI that's accurate, explainable, and built for real-world impact.

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Dr. Mohamed EL ASRI
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Data Source
Sentinel-1 SAR
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Regions
3 Plains
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Architecture
CNN-Mamba
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CNN–Mamba
Hybrid Architecture
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Sentinel-1
SAR Imagery
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Crop Maps
Precision Ag
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Explainable
XAI
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Multi-Temp
Time Series
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Morocco
3 Plains

Powering Crop Intelligence
with 3D Deep Learning

My research combines spatial feature extraction (CNN) with long-range temporal modeling (Mamba) to create hybrid architectures that understand crop growth patterns from space — across seasons, weather, and terrain.

PyTorch Sentinel-1 SAR CNN-Mamba GradCAM Google Earth Engine Multi-Temporal XAI
2
IEEE First-Author Papers
3
Morocco's Agricultural Plains
CNN+Mamba
Hybrid Architecture
2025–28
PhD Research Timeline
About Me

Researching AI for Earth Observation,
From Morocco to the World

Bridging deep learning and satellite physics to build AI that experts can trust.

Research visualization
Deep Learning SAR Remote Sensing Precision Ag Explainable AI

PhD Researcher at Chouaib Doukkali University

I work at the crossroads of deep learning and satellite remote sensing, designing hybrid CNN–Mamba models that classify crops from multi-temporal Sentinel-1 SAR data across the Tadla, Gharb, and Souss-Massa plains.

My focus: building AI that experts can trust — pairing modern deep learning with the physics of how radar interacts with vegetation, so results are both accurate and explainable.

First-author of two IEEE papers, with journal articles in preparation, I collaborate with research teams across Europe and stay active in scientific innovation and entrepreneurship.

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Sentinel-1 SAR

Multi-temporal radar data for all-weather crop monitoring

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CNN–Mamba Hybrids

State-of-the-art architectures for spatial–temporal reasoning

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Precision Agriculture

Crop mapping, irrigation & yield assessment at scale

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Explainable AI

Physics-informed models that experts can trust & interpret

Services

What I Can Build With You

From custom AI architectures to publication-ready research — here's how I can help.

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Deep Learning Model Development

Custom AI architectures — CNN, Mamba, Transformers, and hybrids — built and trained for your remote sensing problem, with a focus on accuracy, robustness, and explainability.

PyTorch CNN-Mamba Transformers XAI
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Crop Classification & Mapping

Large-scale crop type mapping and monitoring from satellite imagery, designed for precision agriculture, irrigation management, and yield assessment across agricultural regions.

Sentinel-1 SAR Multi-temporal GEE
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Scientific Writing & Reviews

From original research articles to systematic reviews — clear, rigorous, and publication-ready scientific writing in remote sensing and deep learning domains.

IEEE Peer Review Systematic Grants
Research Journey

Experience & Education

From Morocco's farmlands to international collaborations — my research path.

2025 – Present

PhD Researcher — Deep Learning & Remote Sensing

LISTM Laboratory, Chouaib Doukkali University

Designing hybrid CNN–Mamba models for crop classification from Sentinel-1 SAR data across Morocco's major agricultural plains. Focused on explainable AI and physics-informed deep learning.

2023 – 2025

IEEE First-Author Publications

International Research Collaborations

Published two first-author IEEE papers on deep learning for remote sensing. Collaborated with research teams across Europe on crop mapping and SAR analysis methodologies.

Ongoing

Scientific Innovation & Entrepreneurship

Cross-disciplinary Applications

Active in translating research into real-world solutions for precision agriculture. Engaged in scientific innovation initiatives and entrepreneurship in AgriTech.

Publications

Selected Research Papers

Peer-reviewed contributions at the intersection of deep learning and Earth observation.

IEEE
1

Hybrid CNN–Mamba for Crop Classification from Multi-Temporal Sentinel-1 SAR Data

M. El Asri et al. — IEEE Conference
First Author
IEEE
2

Deep Learning Approaches for Agricultural Crop Mapping Using Radar Remote Sensing

M. El Asri et al. — IEEE Conference
First Author
IN
PREP

Physics-Informed Explainable AI for Precision Agriculture in Morocco

M. El Asri et al. — Journal Article in Preparation
In Preparation
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Let's Build Something Impactful 🚀

Whether it's a research collaboration, a consulting project, or a conversation about AI for Earth observation — I'm always open to meaningful partnerships.

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