A Comprehensive Review on Computer Vision Analysis of Aerial Data
Vivek Tetarwal, Sandeep Kumar

TL;DR
This comprehensive review covers key computer vision tasks in aerial data analysis, comparing architectures, datasets, and challenges, and discusses future research directions in the field.
Contribution
It provides an extensive overview of methodologies, datasets, and challenges in aerial data computer vision, highlighting gaps and future research opportunities.
Findings
Comparison of hyperparameters across architectures
Discussion of datasets and evaluation metrics
Identification of unresolved challenges and future directions
Abstract
With the emergence of new technologies in the field of airborne platforms and imaging sensors, aerial data analysis is becoming very popular, capitalizing on its advantages over land data. This paper presents a comprehensive review of the computer vision tasks within the domain of aerial data analysis. While addressing fundamental aspects such as object detection and tracking, the primary focus is on pivotal tasks like change detection, object segmentation, and scene-level analysis. The paper provides the comparison of various hyper parameters employed across diverse architectures and tasks. A substantial section is dedicated to an in-depth discussion on libraries, their categorization, and their relevance to different domain expertise. The paper encompasses aerial datasets, the architectural nuances adopted, and the evaluation metrics associated with all the tasks in aerial data…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSatellite Image Processing and Photogrammetry · Advanced Vision and Imaging · Advanced Image Fusion Techniques
MethodsFocus
