GeoAI 与 Python
开源地理空间人工智能实用指南
卫星每天获取海量影像数据，但将像素转化为洞察需要借助人工智能。本书将教你如何使用 Python 和开源工具构建、训练并应用深度学习模型，处理真实的卫星影像。全书包含 23 个可运行的代码章节，帮助你即学即用。所有代码示例均可在 https://book.opengeoai.org 免费获取。
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About the Book
学习如何使用 Python 将深度学习和人工智能应用于卫星影像、航空照片和地理空间数据。本书是一部实践导向的指南，带你从下载遥感数据开始，到训练与评估深度学习模型，全部基于开源工具完成。
你将学到- 使用 Python、PyTorch 和 GPU 加速搭建完整的 GeoAI 环境
- 从 Microsoft Planetary Computer 和开放数据门户下载卫星影像
- 构建交互式地图，并从大规模卫星影像中准备训练数据集
- 针对七类核心地理空间 AI 任务训练与评估模型：图像识别、目标检测、语义分割、实例分割、图像转换、变化检测和像素级回归
- 将基础模型应用于实际问题，包括 Segment Anything Model（SAM）、视觉语言模型以及卫星嵌入表示
- 使用 QGIS 插件在无需编写代码的情况下运行 AI 工作流，例如树木分割、水体检测等
- 全书共 23 章可执行代码示例，分为五个部分：基础知识、数据获取与准备、核心 AI 任务、基础模型和 QGIS 插件
- 所有示例均基于真实卫星影像，结合 PyTorch、torchgeo、segment-geospatial、leafmap 和 geoai 等工具实现
- 所有代码和数据集均可在 GitHub 和 Source Cooperative 免费获取，确保完全可复现
面向希望使用 Python 和开源工具将人工智能应用于地理空间数据的 GIS 从业者、遥感科学家、数据科学家以及学生。
Author
About the Author
Dr. Qiusheng Wu is an Associate Professor at the University of Tennessee and an Amazon Scholar. His research focuses on geospatial data science, with an emphasis on using open-source Python tools and cloud computing to study environmental change. He is the creator of several widely-used Python packages, including geemap, leafmap, and geoai, and is dedicated to advancing geospatial analysis and visualization. Dr. Wu's work is a cornerstone of the open-source geospatial community, and he brings his deep expertise to this book, guiding you through the process of learning and mastering geospatial programming with Python.
Episode 318
An Interview with Qiusheng Wu
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