ArticleFrontiers in artificial intelligence2022
Classification of land use/land cover using artificial intelligence (ANN-RF).
Article in Frontiers in artificial intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Agricultural sustainability monitoring in arid regions using hybrid deep learning and Landsat 8 imagery in Najran City, Saudi Arabia.Scientific reports · 2026Article
- Spatiotemporal land use land cover dynamics and rainfall-runoff responses in the urbanizing Gdynia, Poland.Scientific reports · 2026Article
- Hybrid deep learning and optimization-based land use and land cover classification for advancing sustainable agriculture in Najran city, Saudi Arabia.Scientific reports · 2025Article
- Analyzing histopathological images using fused CNN features based on the geometric active contour method for early diagnosis of lung and colon cancer.Discover oncology · 2025Article
- Evaluating contaminated land and the environmental impact of oil spills in the Niger Delta region: a remote sensing-based approach.Environmental monitoring and assessment · 2025Article
- Improving accuracy of land-use classification through MobileNetV3 and Greedy Osprey Optimization.Scientific reports · 2025Article
- Evaluating the change and trend of construction land in Changsha City based GeoSOS-FLUS model and machine learning methods.Scientific reports · 2025Article
- ENVINet5 deep learning change detection framework for the estimation of agriculture variations during 2012-2023 with Landsat series data.Environmental monitoring and assessment · 2024Article
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Authors and funding
3 authors.
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Abstract
Because deep learning has various downsides, such as complexity, expense, and the need to wait longer for results, this creates a significant incentive and impetus to invent and adopt the notion of developing machine learning because it is simple. This study intended to increase the accuracy of machine-learning approaches for land use/land cover classification using Sentinel-2A, and Landsat-8 satellites. This study aimed to implement a proposed method, neural-based with object-based, to produce a model addressed by artificial neural networks (limited parameters) with random forest (hyperparameter) called ANN_RF. This study used multispectral satellite images (Sentinel-2A and Landsat-8) and a normalized digital elevation model as input datasets for the Sana'a city map of 2016. The results showed that the accuracy of the proposed model (ANN_RF) is better than the ANN classifier with the Sentinel-2A and Landsat-8 satellites individually, which may contribute to the development of machine learning through newer researchers and specialists; it also conventionally developed traditional artificial neural networks with seven to ten layers but with access to 1,000's and millions of simulated neurons without resorting to deep learning techniques (ANN_RF).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.