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DOI：
10.1109/itc-egypt58155.2023.10206340
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发表时间：
2023-07
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农村和农村贫民窟问题是许多发展中国家以及一些发达国家面临的最严峻的问题之一。 2021年，埃及政府启动了埃及农村发展国家倡议——体面生活（Hayah Karima）。这一举措是人道责任和社会层面的结果，因为它的目的不仅仅是改善埃及居民的日常生活和生活状况。本文重点开发一个模型，利用 GeoAI 确定 Asuit 村庄的发展优先事项，因为它被认为是最需要发展的省份之一。 GeoAI 可以被解释为一个分析环境，用于构建模拟人类感知、空间推理以及地理现象和动态发现的智能计算机程序，以获得有关确定研究区域发展优先事项的高级知识。 Shamya村，位于埃及阿苏伊特萨赫勒塞利姆中心；之所以被选为研究区，是因为根据埃及农村村庄发展项目的发展重点，该村被认为是最贫困的村庄之一，贫困率超过70%。这与埃及政府给予上埃及村庄更多考虑的战略是一致的。 GeoAI 用于采用深度学习算法的建筑物和街道足迹自动检测和数字化。评估使用经典技术创建空间地图（需要 40 个工作小时），利用 GeoAI，在 30 分钟内为同一区域生成空间地图，精度更高，工作和质量控制时间更少。事实证明，GeoAI 提供了一种独特且有前途的方法，用于自动检测对象并绘制它们以确定发展轨迹。
The problem of rural villages and slums of the countryside is one of the most critical matters faced by many developing countries as well as some developed countries. In 2021 The Egyptian government launched the National Initiative for the Development of the Egyptian Rural Villages – Decent Life (Hayah Karima). This initiative is the result of a humane responsibility and a social dimension since it has a greater purpose than simply enhancing the daily lives and living situations of Egyptian residents. This paper focuses on developing a model for the determination of the development priorities utilizing GeoAI in the villages of Asuit as it was considered as one of the governorates with the greatest need for development. GeoAI can be construed as an analysis environment to build intelligent computer programs that mimic human perception, spatial reasoning, and discovery of geographic phenomena and dynamics, for advanced knowledge about the determination of development priorities in the study area. Shamya village, which located in Sahel Selim center, Asuit, Egypt; was selected as a study area as it was considered one of the poorest villages according to the development priorities in the Egyptian Rural Villages Development Project, where the poverty rate exceeds more than 70%. This is consistent with the Egyptian government strategy of giving Upper Egypt villages more consideration. GeoAI was utilized for buildings and streets footprint automatic detection and digitizing employing deep learning algorithms. Evaluating the use of classic techniques to create spatial maps, which required 40 working hours, utilizing GeoAI, which produced spatial maps for the same area in 30 minutes with higher precision and with less working and quality control time. It was proven that GeoAI presents a unique and promising approach for automatically detecting objects and mapping them to determine development trajectories.