Limiting the Collection of Ground Truth Data for Land Use and Land Cover Maps with Machine Learning Algorithms
Land use and land cover (LULC) classification maps help understand the state and trends of agricultural production and provide insights for applications in environmental monitoring. One of the major downfalls of the LULC technique is inherently linked to its need for ground truth data to cross-valid...
Egile Nagusiak: | Usman Ali, Travis J. Esau, Aitazaz A. Farooque, Qamar U. Zaman, Farhat Abbas, Mathieu F. Bilodeau |
---|---|
Formatua: | Artikulua |
Hizkuntza: | English |
Argitaratua: |
MDPI AG
2022-06-01
|
Saila: | ISPRS International Journal of Geo-Information |
Gaiak: | |
Sarrera elektronikoa: | https://www.mdpi.com/2220-9964/11/6/333 |
Antzeko izenburuak
-
Evaluation of Different Classification Algorithms for Land Use Land Cover Mapping
nork: Kaifi Chomani, et al.
Argitaratua: (2024-08-01) -
Object-based approaches for land use-land cover classification using high resolution quick bird satellite imagery (a case study: Kerbela, Iraq)
nork: Hussein Sabah Jaber, et al.
Argitaratua: (2022-06-01) -
Sen-2 LULC: Land use land cover dataset for deep learning approaches
nork: Suraj Sawant, et al.
Argitaratua: (2023-12-01) -
A Framework of Filtering Rules over Ground Truth Samples to Achieve Higher Accuracy in Land Cover Maps
nork: Mario Padial-Iglesias, et al.
Argitaratua: (2021-07-01) -
Development of a convolutional neural network to accurately detect land use and land cover
nork: Carolina Acuña-Alonso, et al.
Argitaratua: (2024-06-01)