Impact of Tile Size and Tile Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification

Autor: Calimanut-Ionut Cira, Miguel-Ángel Manso-Callejo, Naoto Yokoya, Tudor Sălăgean, Ana-Cornelia Badea

Publikováno v: Remote Sensing, Vol 16, Iss 15, p 2818 (2024)

Popular geo-computer vision works make use of aerial imagery, with sizes ranging from 64 × 64 to 1024 × 1024 pixels without any overlap, although the learning process of deep learning models can be affected by the reduced semantic context or the la

Externí odkaz: https://doaj.org/article/0a0f715b61654bd1a53a4fdceb693beb

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Land-Cover-Change Detection with Aerial Orthoimagery Using SegNet-Based Semantic Segmentation in Namyangju City, South Korea

Autor: Sanghun Son, Seong-Hyeok Lee, Jaegu Bae, Minji Ryu, Doi Lee, So-Ryeon Park, Dongju Seo, Jinsoo Kim

Publikováno v: Sustainability; Volume 14; Issue 19; Pages: 12321

In this study, we classified land cover using SegNet, a deep-learning model, and we assessed its classification accuracy in comparison with the support-vector-machine (SVM) and random-forest (RF) machine-learning models. The land-cover classification

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fada97a811a465184958f2b73df3610c

A Deep Learning-Based Solution for Large-Scale Extraction of the Secondary Road Network from High-Resolution Aerial Orthoimagery

Autor: Francisco Serradilla, Miguel-Ángel Manso-Callejo, Ramón Alcarria, Calimanut-Ionut Cira

Publikováno v: Applied Sciences
Volume 10
Issue 20
Applied Sciences, Vol 10, Iss 7272, p 7272 (2020)
Machine Learning and Remote Sensing for Automatic Map Creation and Update, ISSN 2076-3417, 2020-10-17, Vol. 10, No. 20
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Secondary roads represent the largest part of the road network. However, due to the absence of clearly defined edges, presence of occlusions, and differences in widths, monitoring and mapping them represents a great effort for public administration.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b2f2f9c4858cabbfb344abcd507e8aec

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