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Ditemukan 3 dari pencarian Anda melalui kata kunci: subject="eCognition"
cover
Geological object recognition in legacy maps through data augmentation and tr…
Komentar Bagikan
Xiaogang MaJiyin ZhangWenjia LiWeilin ChenChenhao Li

Maps are crucial tools in geosciences, providing detailed representations of the spatial distribution and relationships among geological features. Accurate recognition and classification of geological objects within these maps are essential for applications in resource exploration, environmental management, and geological hazard assessment. Along the years, many legacy geological maps have been…

Edisi
Vol.25, February 2025
ISBN/ISSN
2590-1974
Deskripsi Fisik
8 hlm PDF, 4.999 KB
Judul Seri
Applied Computing and Geoscience - Open Access
No. Panggil
551.136
cover
A new approach to dust source mapping using visual interpretation and object-…
Komentar Bagikan
Ali Darvishi BolooraniNastaran NasiriMasoud SoleimaniRamin PapiFatemeh AmiriNajmeh Neysani SamanyAzher Ibrahim Al-TaeiSaham MirzaeiAli Al-Hemoud

The emission of dust particles, mainly from arid and semi-arid lands, as a result of climate change and human activities, is known to be a global issue. Identifying dust emission sources is the first key step in dealing with the hazardous consequences of this rising phenomenon. This study is an attempt to address one of the major challenges in mapping dust emission sources. Accordingly, an inno…

Edisi
Vol.23, September 2024
ISBN/ISSN
2590-1974
Deskripsi Fisik
9 hlm PDF, 13.165 KB
Judul Seri
Applied Computing and Geoscience - Open Access
No. Panggil
551.136
cover
A novel few-shot learning framework for rock images dually driven by data and…
Komentar Bagikan
Zhongliang ChenFeng YuanXiaohui LiMingming ZhangChaojie Zheng

In the field of geosciences, the integration of artificial intelligence is transitioning from perceptual intelligence to cognitive intelligence. The simultaneous utilization of knowledge and data in the geoscience domain is a universally addressed concern. In this paper, based on the interpretability of deep learning models for rock images, rock features such as structure, texture, mineral and …

Edisi
Vol.21, March 2024
ISBN/ISSN
2590-1974
Deskripsi Fisik
11 hlm PDF, 6.795 KB
Judul Seri
Applied Computing and Geoscience - Open Access
No. Panggil
551.136