GeoAI mit Python
Ein praxisorientierter Leitfaden für Open-Source-Geodaten-KI
Satelliten erfassen täglich enorme Mengen an Bilddaten, doch um aus Pixeln Erkenntnisse zu gewinnen, ist KI erforderlich. Dieses Buch zeigt Ihnen, wie Sie Deep-Learning-Modelle für reale Satellitenbilder mit Python und Open-Source-Tools entwickeln, trainieren und anwenden können. Es enthält 23 Kapitel mit ausführbarem Code, den Sie sofort nutzen können. Alle Codebeispiele sind frei verfügbar unter https://book.opengeoai.org.
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About the Book
Lernen Sie, wie Sie Deep Learning und künstliche Intelligenz auf Satellitenbilder, Luftaufnahmen und geospatiale Daten mit Python anwenden. Dieser praxisnahe Leitfaden führt Sie von der Beschaffung von Fernerkundungsdaten bis hin zum Training und zur Evaluierung von Deep-Learning-Modellen, alles mit Open-Source-Tools.
Was Sie lernen werden- Ein vollständiges GeoAI-Umfeld mit Python, PyTorch und GPU-Beschleunigung einrichten
- Satellitenbilder vom Microsoft Planetary Computer und aus offenen Datenportalen herunterladen
- Interaktive Karten erstellen und Trainingsdatensätze aus großen Satellitenbildern vorbereiten
- Modelle für sieben zentrale Aufgaben der geospatialen KI trainieren und evaluieren: Bilderkennung, Objekterkennung, semantische Segmentierung, Instanzsegmentierung, Bildübersetzung, Veränderungserkennung und Regression auf Pixelebene
- Foundation-Modelle, einschließlich des Segment Anything Model (SAM), Vision-Language-Modelle und Satelliten-Embeddings, auf reale Erdbeobachtungsprobleme anwenden
- KI-Workflows in QGIS ohne Programmierung mithilfe von Plugins für Baumsegmentierung, Wasserdetektion und mehr ausführen
- 23 Kapitel mit ausführbaren Codebeispielen, organisiert in fünf Teile: Grundlagen, Datenerfassung und -vorbereitung, zentrale KI-Aufgaben, Foundation-Modelle und QGIS-Plugins
- Alle Beispiele verwenden reale Satellitenbilder mit PyTorch, torchgeo, segment-geospatial, leafmap und geoai
- Sämtlicher Code und alle Datensätze sind frei auf GitHub und Source Cooperative verfügbar, um vollständige Reproduzierbarkeit zu gewährleisten
GIS-Fachleute, Fernerkundungswissenschaftler, Data Scientists und Studierende, die KI mit Python und Open-Source-Tools auf geospatiale Daten anwenden möchten.
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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