GeoAI with Python
オープンソース地理空間AIのための実践ガイド
衛星は毎日膨大な量の画像を取得していますが、ピクセルから洞察を得るにはAIが必要です。本書では、Pythonとオープンソースツールを用いて、実際の衛星画像に対してディープラーニングモデルを構築・学習・適用する方法を解説します。すぐに実行できるコードを含む23章で構成されており、すべてのコード例は https://book.opengeoai.org で無料公開されています。
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About the Book
Pythonを使って、衛星画像、航空写真、地理空間データにディープラーニングとAIを適用する方法を学びます。本書は、リモートセンシングデータの取得からディープラーニングモデルの学習と評価まで、すべてをオープンソースツールで実践的に解説します。
学べる内容- Python、PyTorch、GPUアクセラレーションを用いたGeoAI環境の構築
- Microsoft Planetary Computerやオープンデータポータルからの衛星画像の取得
- 大規模な衛星画像からインタラクティブな地図を作成し、学習用データセットを準備する方法
- 画像認識、物体検出、セマンティックセグメンテーション、インスタンスセグメンテーション、画像変換、変化検出、ピクセル単位の回帰という7つの主要な地理空間AIタスクのためのモデルの学習と評価
- Segment Anything Model（SAM）、ビジョン・ランゲージモデル、衛星エンベディングなどの基盤モデルを実世界の地球観測問題に適用する方法
- QGISのプラグインを使用して、コードを書かずに樹木セグメンテーションや水域検出などのAIワークフローを実行する方法
- 実行可能なコード例を含む23章で構成され、5つのパート（基礎、データ取得と前処理、主要AIタスク、基盤モデル、QGISプラグイン）に整理
- すべての例は、PyTorch、torchgeo、segment-geospatial、leafmap、geoaiを用いた実際の衛星画像を使用
- すべてのコードとデータセットはGitHubおよびSource Cooperativeで無料公開されており、完全な再現性を実現
GIS専門家、リモートセンシング研究者、データサイエンティスト、そしてPythonとオープンソースツールを用いて地理空間データにAIを適用したい学生向け。
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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