Evaluation of coarse aggregate properties in hardened concrete based on segment anything model (SAM) DOI
Seung‐Woon Baek, Sooyoon Koh,

W.J. Kim

et al.

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 453, P. 139013 - 139013

Published: Nov. 1, 2024

Language: Английский

Time-series growth estimation of sea cucumbers (Apostichopus japonicus) in co-cultivation with coho salmon using time-lapse cameras in field experiments DOI
Takero Yoshida,

Kasumi Kogo

Aquacultural Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 102529 - 102529

Published: March 1, 2025

Language: Английский

Citations

0
A novel architecture for automated delineation of the agricultural fields using partial training data in remote sensing images DOI Creative Commons
Sumesh KC, Jagannath Aryal, Dongryeol Ryu

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 234, P. 110265 - 110265

Published: March 18, 2025

Language: Английский

Citations

0
Erosion-SAM: Semantic segmentation of soil erosion by water DOI Creative Commons
Hadi Shokati, Andreas Engelhardt,

Kay Seufferheld

et al.

CATENA, Journal Year: 2025, Volume and Issue: 254, P. 108954 - 108954

Published: March 23, 2025

Language: Английский

Citations

0
Contrastive Activation Maps with Superpixel Rectification for Weakly Supervised Semantic Segmentation: When Superpixels Meet CAMs DOI

H. Shi,

Yukun Liu,

Shaofan Wang

et al.

Digital Signal Processing, Journal Year: 2025, Volume and Issue: unknown, P. 105196 - 105196

Published: March 1, 2025

Language: Английский

Citations

0
Integrating unsupervised domain adaptation and SAM technologies for image semantic segmentation: a case study on building extraction from high-resolution remote sensing images DOI Creative Commons

Mengyuan Yang,

Rui Yang, Min Wang

et al.

International Journal of Digital Earth, Journal Year: 2025, Volume and Issue: 18(1)

Published: April 15, 2025

Language: Английский

Citations

0
Machine Learning-Based Summer Crops Mapping Using Sentinel-1 and Sentinel-2 Images DOI Creative Commons
Saeideh Maleki, Nicolas Baghdadi, Hassan Bazzi

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(23), P. 4548 - 4548

Published: Dec. 4, 2024

Accurate crop type mapping using satellite imagery is crucial for food security, yet accurately distinguishing between crops with similar spectral signatures challenging. This study assessed the performance of Sentinel-2 (S2) time series (spectral bands and vegetation indices), Sentinel-1 (S1) (backscattering coefficients polarimetric parameters), alongside phenological features derived from both S1 S2 (harmonic median features), classifying sunflower, soybean, maize. Random Forest (RF), Multi-Layer Perceptron (MLP), XGBoost classifiers were applied across various dataset configurations train-test splits over two sites years in France. Additionally, InceptionTime classifier, specifically designed data, was tested exclusively datasets to compare its against three general machine learning algorithms (RF, XGBoost, MLP). The results showed that outperformed RF MLP crops. optimal all combined backscattering indices, comparable data (mean F1 scores 89.9% 76.6% 91.1% maize). However, when individual sensors, while superior soybean Both produced close mean spatial, temporal, spatiotemporal transfer scenarios, though best choice transfer. Polarimetric did not yield effective results. classifier further improved classification accuracy crops, degree improvement varying by (the highest 90.6% 86.0% 93.5%

Language: Английский

Citations

1
Evaluation of coarse aggregate properties in hardened concrete based on segment anything model (SAM) DOI
Seung‐Woon Baek, Sooyoon Koh,

W.J. Kim

et al.

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 453, P. 139013 - 139013

Published: Nov. 1, 2024

Language: Английский

Citations

0