2023 SAG Award Winners

Ministry of Agriculture and Land Reclamation

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Project Goal

The solution integrates satellite imagery, field observations, data feeds, and GeoAI analytical capabilities to support Agricultural Research Center (ARC) in maximizing the various crops' yield from limited resources (agricultural land). This is achieved through field boundary detection and crop type detection and classification. The solution provides imagery analysis workflows that leverage Esri geospatial platform capabilities and utilize Azure AI products and services, namely: Azure GeoAI Data Science Virtual Machine (DSVM) and Azure Custom Vision Service.
The agricultural fields in Egypt are commonly distributed with relatively small sizes parcels that usually reduce the reliability of agricultural statistics in surveying cropland. Therefore, the identification of crop types will be implemented by using time series analysis of remote sensing data reflecting crop classification. This will support building an accurate crop inventory under complex landscape conditions based using GIS
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Business Problem Solved

Classify land cover into pervious and impervious surfaces using deep learning-based Pixel Classification models.
Get the knowledge of the current state of the landscape, understand current land cover and how it is being used, along with an accurate means of monitoring change over time.
Land cover maps have been extensively used for a variety of applications including Agriculture lands change monitoring, Urban Planning, Ecosystem services, Climate change, Hydrological processes, and policymaking at local and regional scales

Technology Implemented

ArcGIS Pro
Microsoft Azure, ArcGIS Online or ArcGIS Enterprise
ArcGIS API for Python for training

Development Team Biography

Islam Saeed (Head Of Enterpries Team)
Aliaa Osama (Produects Manager)
Ahmed Hefnawy (AI consultant)
Khaled Talaat (Progect Manager)
Dina Ahmed (Remote Sensenig)
Dr. Mohamed Elkersh (Project Coordinator)