Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

SEMANTIC SEGMENTATION OF BURNED AREAS IN SATELLITE IMAGES USING A U-NET-BASED CONVOLUTIONAL NEURAL NETWORK

Domaine:

geospatialenvironment and energy

Type de record:

paper
Créateur:
A. A.
Éditeur:
Cop
Hôte:
Abstract. The use of remote sensing data for burned area mapping hast led to unprecedented advances within the field in recent years. Although threshold and traditional machine learning based methods have successfully been applied to the task, they implicate drawbacks including the involvement of complex rule sets and requirement of previous feature engineering. In contrast, deep learning offers an end-to-end solution for image analysis and semantic segmentation. In this study, a variation of U-Net is investigated for mapping burned areas in mono-temporal Sentinel-2 imagery. The experimental setup is divided into two phases. The first one includes a performance evaluation based on test data, while the second serves as a use case simulation and spatial evaluation of training data quality. The former is especially designed to compare the results between two local (trained only with data from the respective research areas) and a global (trained with the whole dataset) variant of the model with research areas being Indonesia and Central Africa. The networks are trained from scratch with a manually generated customized training dataset. The application of the two variants per region revealed only slight superiority of the local model (macro-F1: 92%) over the global model (macro-F1: 91%) in Indonesia with no difference in overall accuracy (OA) at 94%. In Central Africa, the results of the global and local model are the same in both metrics (OA: 84%, macro-F1: 82%). Overall, the outcome demonstrates the global model’s ability to generalize despite high dissimilarities between the research areas.

Visit

doi.org

Tasks

computer visionimage classification

Licenses

https://creativecommons.org/licenses/by/4.0/

Similaires

Using satellite images to monitor burned areas in MadagascargrassUNet: a U-Net model for semantic segmentation of upward-facing plant canopy images using low-cost instrumentationImage segmentation of cervical grainy sandy patches lesions associated with female genital schistosomiasis using deep convolutional neural network with U-NET architectureCEU-Net: Ensemble Semantic Segmentation of Hyperspectral Images Using ClusteringRURAL SETTLEMENTS SEGMENTATION BASED ON DEEP LEARNING U-NET USING REMOTE SENSING IMAGES تقسيم المستوطنات الريفية على أساس التعلم العميق U - NET باستخدام صور الاستشعار عن بعد RURAL SETTLEMENTS SEGMENTATION BASED ON DEEP LEARNING U-NET USING REMOTE SENSING IMAGES SEGMENTATION DES RÈGLEMENTS RURAUX BASÉE SUR L'APPRENTISSAGE EN PROFONDEUR U-NET À L'AIDE D'IMAGES DE DÉTECTION À DISTANCE RURAL SETTLEMENTS SEGMENTATION BASED ON DEEP LEARNING U-NET USING REMOTE SENSING IMAGESDRD-Net: Diabetic Retinopathy Diagnosis Using A Hybrid Convolutional Neural Network

Using satellite images to monitor burned areas in Madagascar

Mapping the extent of fire-affected areas is very important for integrated fire management, especial

grassUNet: a U-Net model for semantic segmentation of upward-facing plant canopy images using low-cost instrumentation

Accurate and cost-effective measurement of canopy variables such as the Plant Area In

Image segmentation of cervical grainy sandy patches lesions associated with female genital schistosomiasis using deep convolutional neural network with U-NET architecture

Female genital schistosomiasis (FGS) is a neglected but highly prevalent disease in sub-Saharan Afri

CEU-Net: Ensemble Semantic Segmentation of Hyperspectral Images Using Clustering

Most semantic segmentation approaches of Hyperspectral images (HSIs) use and require preprocessing s

RURAL SETTLEMENTS SEGMENTATION BASED ON DEEP LEARNING U-NET USING REMOTE SENSING IMAGES تقسيم المستوطنات الريفية على أساس التعلم العميق U - NET باستخدام صور الاستشعار عن بعد RURAL SETTLEMENTS SEGMENTATION BASED ON DEEP LEARNING U-NET USING REMOTE SENSING IMAGES SEGMENTATION DES RÈGLEMENTS RURAUX BASÉE SUR L'APPRENTISSAGE EN PROFONDEUR U-NET À L'AIDE D'IMAGES DE DÉTECTION À DISTANCE RURAL SETTLEMENTS SEGMENTATION BASED ON DEEP LEARNING U-NET USING REMOTE SENSING IMAGES

Abstract. Accurate and efficient extraction of rural settlements from high-resolution remote sensing

DRD-Net: Diabetic Retinopathy Diagnosis Using A Hybrid Convolutional Neural Network

Diabetic Retinopathy (DR) has become a leading cause of blindness among diabetic patients. Accurate