IM2ELEVATION: Building Height Estimation from Single-View Aerial Imagery
Remote Sensing, 2020 Estimation of the Digital Surface Model (DSM) and building heights from single-view aerial imagery is a challenging inherently ill-posed problem that we address in this paper by resorting to machine learning.
Chao-Jung Liu +4 more
doaj +2 more sources
Object Detection in Aerial Imagery [PDF]
arXiv.org, 2022 Technical ...
Demidov, Dmitry +2 more
openaire +3 more sources
Lightweight Object Detection Algorithm for UAV Aerial Imagery. [PDF]
Sensors (Basel), 2023 Addressing the challenges of low detection precision and excessive parameter volume presented by the high resolution, significant scale variations, and complex backgrounds in UAV aerial imagery, this paper introduces MFP-YOLO, a lightweight detection ...
Wang J +4 more
europepmc +2 more sources
Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar [PDF]
Remote Sensing of Environment, 2023 Vegetation structure mapping is critical for understanding the global carbon cycle and monitoring nature-based approaches to climate adaptation and mitigation.
James M. Tolan +15 more
semanticscholar +1 more source
Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental Comparisons [PDF]
Engineering applications of artificial intelligence, 2023 With the advancement of maritime unmanned aerial vehicles (UAVs) and deep learning technologies, the application of UAV-based object detection has become increasingly significant in the fields of maritime industry and ocean engineering.
Chenjie Zhao +3 more
semanticscholar +1 more source
Physical Adversarial Attacks on an Aerial Imagery Object Detector [PDF]
IEEE Workshop/Winter Conference on Applications of Computer Vision, 2021 Deep neural networks (DNNs) have become essential for processing the vast amounts of aerial imagery collected using earth-observing satellite platforms. However, DNNs are vulnerable towards adversarial examples, and it is expected that this weakness also
Andrew Du +6 more
semanticscholar +1 more source
Urban Vegetation Mapping from Aerial Imagery Using Explainable AI (XAI). [PDF]
Sensors (Basel), 2021 Urban vegetation mapping is critical in many applications, i.e., preserving biodiversity, maintaining ecological balance, and minimizing the urban heat island effect. It is still challenging to extract accurate vegetation covers from aerial imagery using
Abdollahi A, Pradhan B.
europepmc +2 more sources
Cotton Yield Estimation From Aerial Imagery Using Machine Learning Approaches. [PDF]
Front Plant Sci, 2022 Estimation of cotton yield before harvest offers many benefits to breeding programs, researchers and producers. Remote sensing enables efficient and consistent estimation of cotton yields, as opposed to traditional field measurements and surveys.
Rodriguez-Sanchez J, Li C, Paterson AH.
europepmc +2 more sources
Enhanced Single Shot Small Object Detector for Aerial Imagery Using Super-Resolution, Feature Fusion and Deconvolution. [PDF]
Sensors (Basel), 2022 One common issue of object detection in aerial imagery is the small size of objects in proportion to the overall image size. This is mainly caused by high camera altitude and wide-angle lenses that are commonly used in drones aimed to maximize the ...
Maktab Dar Oghaz M +2 more
europepmc +2 more sources
Deep Learning Models for the Classification of Crops in Aerial Imagery: A Review
Agriculture, 2023 In recent years, the use of remote sensing data obtained from satellite or unmanned aerial vehicle (UAV) imagery has grown in popularity for crop classification tasks such as yield prediction, soil classification or crop mapping.
Igor Teixeira +3 more
semanticscholar +1 more source