Active Learning For Semantic Labelling Of Airborne Lidar Data
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Active Learning for Semantic Labelling of Airborne LIDAR Data
Creating training data for classification of airborne LIDAR data is expensive and time-consuming. To label training data, ground truth data is gathered via field surveys or human photo-interpretation of aerial imagery. To avoid getting poor end results due to insufficient training data, organizations often label more training data than is actually needed - at a large expense. Using a semi-supervised, active learning approach for both the segmentation and classification of human-made objects and vegetation in urban, airborne LIDAR point clouds, as is proposed in this work, allows a minimal training data set to be created, tested, and expanded in key areas, as-needed, in an interactive, iterative process. The active learning iterations for segmentation gather linkage constraints to apply on the hierarchical clustering. Then, the number of segments is estimated using an enhanced L-method. The active learning iterations for classification gather additional training patches in uncertain areas according to the SVM results.
Deep Learning for Multi-Sensor Earth Observation
Deep Learning for Multi-Sensor Earth Observation addresses the need for transformative Deep Learning techniques to navigate the complexity of multi-sensor data fusion. With insights drawn from the frontiers of remote sensing technology and AI advancements, it covers the potential of fusing data of varying spatial, spectral, and temporal dimensions from both active and passive sensors. This book offers a concise, yet comprehensive, resource, addressing the challenges of data integration and uncertainty quantification from foundational concepts to advanced applications. Case studies illustrate the practicality of deep learning techniques, while cutting-edge approaches such as self-supervised learning, graph neural networks, and foundation models chart a course for future development.Structured for clarity, the book builds upon its own concepts, leading readers through introductory explanations, sensor-specific insights, and ultimately to advanced concepts and specialized applications. By bridging the gap between theory and practice, this volume equips researchers, geoscientists, and enthusiasts with the knowledge to reshape Earth observation through the dynamic lens of deep learning. - Addresses the problem of unwieldy datasets from multi-sensor observations, applying Deep Learning to multi-sensor data integration from disparate sources with different resolution and quality - Provides a thorough foundational reference to Deep Learning applications for handling Earth Observation multi-sensor data across a variety of geosciences - Includes case studies and real-world data/examples allowing readers to better grasp how to put Deep Learning techniques and methods into practice
Geospatial Intelligence
Author: Fatimazahra Barramou
language: en
Publisher: Springer Nature
Release Date: 2021-11-10
This book explores cutting-edge methods combining geospatial technologies and artificial intelligence related to several fields such as smart farming, urban planning, geology, transportation, and 3D city models. It introduces techniques which range from machine and deep learning to remote sensing for geospatial data analysis. The book consists of two main parts that include 13 chapters contributed by promising authors. The first part deals with the use of artificial intelligence techniques to improve spatial data analysis, whereas the second part focuses on the use of artificial intelligence with remote sensing in various fields. Throughout the chapters, the interest for the use of artificial intelligence is demonstrated for different geospatial technologies such as aerial imagery, drones, Lidar, satellite remote sensing, and more. The work in this book is dedicated to the scientific community interested in the coupling of geospatial technologies and artificial intelligence and exploring the synergetic effects of both fields. It offers practitioners and researchers from academia, the industry and government information, experiences and research results about all aspects of specialized and interdisciplinary fields on geospatial intelligence.