GeoAI com Python
Um guia prático de IA geoespacial de código aberto
Satélites capturam enormes volumes de imagens todos os dias, mas transformar pixels em conhecimento requer IA. Este livro ensina você a construir, treinar e aplicar modelos de aprendizado profundo a imagens de satélite reais usando Python e ferramentas de código aberto, com 23 capítulos de código executável que você pode usar hoje mesmo. Todos os exemplos de código estão disponíveis gratuitamente em https://book.opengeoai.org.
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About the Book
Aprenda a aplicar aprendizado profundo e inteligência artificial a imagens de satélite, fotos aéreas e dados geoespaciais usando Python. Este guia prático conduz você desde o download de dados de sensoriamento remoto até o treinamento e avaliação de modelos de aprendizado profundo, tudo utilizando ferramentas de código aberto.
O que você aprenderá- Configurar um ambiente completo de GeoAI com Python, PyTorch e aceleração por GPU
- Baixar imagens de satélite do Microsoft Planetary Computer e de portais de dados abertos
- Criar mapas interativos e preparar conjuntos de dados de treinamento a partir de imagens de satélite de grande escala
- Treinar e avaliar modelos para sete tarefas principais de IA geoespacial: reconhecimento de imagens, detecção de objetos, segmentação semântica, segmentação de instâncias, tradução de imagens, detecção de mudanças e regressão em nível de pixel
- Aplicar modelos fundamentais, incluindo o Segment Anything Model (SAM), modelos visão-linguagem e embeddings de satélite, a problemas reais de observação da Terra
- Executar fluxos de trabalho de IA no QGIS sem escrever código, usando plugins para segmentação de árvores, detecção de água e muito mais
- 23 capítulos com exemplos de código executável organizados em cinco partes: Fundamentos, Aquisição e Preparação de Dados, Tarefas Principais de IA, Modelos Fundamentais e Plugins QGIS
- Todos os exemplos utilizam imagens de satélite reais com PyTorch, torchgeo, segment-geospatial, leafmap e geoai
- Todo o código e os conjuntos de dados estão disponíveis gratuitamente no GitHub e no Source Cooperative para garantir total reprodutibilidade
Profissionais de GIS, cientistas de sensoriamento remoto, cientistas de dados e estudantes que desejam aplicar IA a dados geoespaciais usando Python e ferramentas de código aberto.
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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