Ditapis dengan
Contribution of multispectral (optical and radar) satellite images to the cla…
The monitoring of different crops (cultivated plots) and types of surface (bare soils, etc.) is a crucial economic and environmental issue for the management of resources and human activity. In this context, the objective of this study is to evaluate the contribution of multispectral satellite imagery (optical and radar) to land use and land cover classification. Object-oriented supervised cla…
- Edisi
- Vol.84, February 2020
- ISBN/ISSN
- 1569-8432
- Deskripsi Fisik
- 13 hlm PDF, 5.659 KB
- Judul Seri
- International Journal of Applied Earth Observation and Geoinformation - Open Access
- No. Panggil
- 910.285
A convolutional recurrent neural network for strong convective rainfall nowca…
Strong convective systems and the associated heavy rainfall events can trig-ger floods and landslides with severe detrimental consequences. These events have a high spatio-temporal variability, being difficult to predict by standard meteorological numerical models. This work proposes the M5Images method for performing the very short-term prediction (nowcasting) of heavy convective rainfall usin…
- Edisi
- Vol.3, December 2022
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 6 hlm PDF, 1.368 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Flood susceptibility assessment using artificial neural networks in Indonesia
Flood incidents can massively damage and disrupt a city economic or governing core. However, flood risk can be mitigated through event planning and city-wide preparation to reduce damage. For, governments, firms, and civilians to make such preparations, flood susceptibility predictions are required. To predict flood susceptibility nine environmental related factors have been identified. They ar…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 8 hlm PDF, 1.503 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
CloudSense: A model for cloud type identification using machine learning from…
The knowledge of type of precipitating cloud is crucial for radar based quantitative estimates of precipitation. We propose a novel model called CloudSense which uses machine learning to accurately identify the type of precipitating clouds over the complex terrain locations in the Western Ghats (WG) of India. CloudSense uses vertical reflectivity profiles collected during July–August 2018 fro…
- Edisi
- Vol.24, December 2024
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 12 hlm PDF, 6.755 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Transfer learning and single-polarized SAR image preprocessing for oil spill …
This study addresses the challenge of oil spill detection using Synthetic Aperture Radar (SAR) satellite imagery, employing deep learning techniques to improve accuracy and efficiency. We investigated the effectiveness of various neural network architectures and encoders for this task, focusing on scenarios with limited training data. The research problem centered on enhancing feature extractio…
- Edisi
- Vol.15, January 2025
- ISBN/ISSN
- 1872-8235
- Deskripsi Fisik
- 8 hlm PDF, 1.441 KB
- Judul Seri
- ISPRS Open Journal of Photogrammetry and Remote Sensing
- No. Panggil
- 621.3678
Deep learning approach for Sentinel-1 surface water mapping leveraging
Satellite remote sensing plays an important role in mapping the location and extent of surface water. A variety of approaches are available for mapping surface water, but deep learning approaches are not commonplace as they are ‘data hungry’ and require large amounts of computational resources. However, with the availability of various satellite sensors and rapid development in cloud comput…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 1872-8235
- Deskripsi Fisik
- 13 hlm PDF, 4.033 KB
- Judul Seri
- ISPRS Open Journal of Photogrammetry and Remote Sensing
- No. Panggil
- 621.3678
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