Geographic Object-Based Image Analysis: State-of-the-Art and Emerging Research Trends

Dear Colleagues,

Geographic object-based image analysis (GEOBIA) has evolved over the past couple of decades motivated by the increased availability of higher spatial resolution imagery, computing power, analysis algorithms, and application needs. Years ago, some researchers described GEOBIA as a new paradigm in remote sensing image analysis. Today, we have experienced the use of GEOBIA in a wide range of applications using spectral and nonspectral (e.g., Lidar) datasets. An increasing number of commercial software packages are currently implementing GEOBIA algorithms capable of handling large datasets through optimized algorithms and parallel/cloud computations in a production environment. Traditional GEOBIA research involves developing new image segmentation algorithms, optimization segmentation parameters, feature extraction methods, and classification algorithms, as well as experimenting with accuracy assessment methods. The use of deep learning networks in GEOBIA and the recent introduction of deep learning algorithms capable of segmenting and classifying imagery as an emerging subject that integrates several crucial GEOBIA operations in convolutional network frameworks are also welcomed in this Special Issue.

The objective of this Special Issue is to present GEOBIA applications that incorporate recent developments for segmentation, classification, feature extraction, or segmentation parameter selection algorithms. It is our view that this Special Issue provides a timely and valuable opportunity for geo-information community researchers to rethink and advance the GEOBIA workflow by reaping the benefits of recent technology developments, especially in the deep learning area. In this context, we would like to invite our colleagues to submit their GEOBIA studies, in, but not limited to, the following topics:

• Image segmentation and segmentation parameter optimization algorithms;
• Integration of deep learning algorithms in GEOBIA workflow;
• Applications of deep learning semantic segmentation algorithm in the geospatial analysis field;
• Sampling strategies to train or evaluate deep learning classifiers;
• GEOBIA usage in urban and natural land cover/use mapping and change detection applications;
• Algorithms for GEOBIA of Big Data;
• GEOBIA applications in large scale production emphasizing Big Data and cloud computing;
• GEOBIA classification assessment metrics and methods.

Dr. Amr Abd-Elrahman
Dr. Zoltan Szantoi
Dr. Tao Liu
Guest Editors

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• geographic object-based image analysis (GEOBIA)
• OBIA segmentation
• artificial intelligence
• OBIA classification
• deep learning
• big data