GeoAI with Python
Una guida pratica all’intelligenza artificiale geospaziale open source
I satelliti acquisiscono ogni giorno enormi volumi di immagini, ma trasformare i pixel in conoscenza richiede l’IA. Questo libro ti insegna a costruire, addestrare e applicare modelli di deep learning a immagini satellitari reali utilizzando Python e strumenti open source, con 23 capitoli di codice eseguibile che puoi utilizzare fin da subito. Tutti gli esempi di codice sono disponibili gratuitamente su https://book.opengeoai.org.
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About the Book
Impara ad applicare il deep learning e l’intelligenza artificiale a immagini satellitari, fotografie aeree e dati geospaziali utilizzando Python. Questa guida pratica e concreta ti accompagna dal download dei dati di telerilevamento fino all’addestramento e alla valutazione dei modelli di deep learning, utilizzando esclusivamente strumenti open source.
Cosa imparerai- Configurare un ambiente GeoAI completo con Python, PyTorch e accelerazione GPU
- Scaricare immagini satellitari da Microsoft Planetary Computer e portali di dati aperti
- Creare mappe interattive e preparare dataset di addestramento a partire da immagini satellitari di grandi dimensioni
- Addestrare e valutare modelli per sette attività fondamentali di AI geospaziale: riconoscimento di immagini, rilevamento di oggetti, segmentazione semantica, segmentazione di istanze, traduzione di immagini, rilevamento dei cambiamenti e regressione a livello di pixel
- Applicare modelli fondamentali, tra cui il Segment Anything Model (SAM), modelli visione-linguaggio ed embedding satellitari, a problemi reali di osservazione della Terra
- Eseguire workflow di IA in QGIS senza scrivere codice utilizzando plugin per la segmentazione degli alberi, il rilevamento dell’acqua e altro
- 23 capitoli con esempi di codice eseguibile organizzati in cinque parti: Fondamenti, Acquisizione e preparazione dei dati, Attività principali di IA, Modelli fondamentali e Plugin QGIS
- Tutti gli esempi utilizzano immagini satellitari reali con PyTorch, torchgeo, segment-geospatial, leafmap e geoai
- Tutto il codice e i dataset sono disponibili gratuitamente su GitHub e Source Cooperative per garantire la piena riproducibilità
Professionisti GIS, scienziati della telerilevazione, data scientist e studenti che desiderano applicare l’IA ai dati geospaziali utilizzando Python e strumenti open source.
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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