A super-ensemble approach to map land cover types with high resolution over data-sparse African savanna landscapes

Song, Lei; Estes, Anna B.; Estes, Lyndon

doi:10.1016/j.jag.2022.103152

Cited by 2 publications

(3 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast, Šćepanović et al, [69] has used freely available high-resolution CORINE map [70] having five land cover features which are mainly generated for European and seven cooperating countries [70]; Song et al, [28] used Globe base map having seven land cover features. The authors of the papers [32,71] used the dataset provided by the Government agency and freely available data.…”

Section: Data Annotation Approachesmentioning

confidence: 99%

“…The authors of the papers [1,25,32] integrated SAR and Optical images for land cover classification using the Random Forest algorithm. Song et al, [28] used U-Net for classifying seven land cover features.…”

Section: Classification Of Sar Images Using Deep Learning Approachesmentioning

confidence: 99%

“…Electronic advancements in sensors and satellite platforms allow satellite spatial and temporal resolutions to continue to rise, reducing the time window for capturing information about a specific point on the Earth's surface and allowing for more continuous monitoring and analysis of the dynamics of various territories around the world. This temporal window is critical for agricultural area classification research, particularly for seasonal crops [25,27] Across the past 20 years, new opportunities for rapid mapping of land cover over vast areas have steadily emerged thanks to satellite photography with excellent spatial and temporal resolution [28]. The Copernicus Program, funded by the European Space Agency (ESA), now offers Sentinel, a global monitoring satellite platform that captures images of the entire Earth's surface every six days in various regions of the electromagnetic spectrum.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Performance Evaluation of DL-based Models for LULC segmentation using SAR time series data of regions in Gujarat

Jain

Varghese²,

Joshi

et al. 2023

Preprint

View full text Add to dashboard Cite

Both spatial and non-spatial data are critical to a country's social and economic development. Land use and land cover (LULC) maps are data that can be used to retrieve land information and detect environmental changes. Several recent studies have used deep learning approaches to generate LULC maps from various remote-sensing image formats. They have correctly recognized several land cover elements such as built-up areas, forests, water, etc. On the other hand, spaceborne radar images must be used to test deep learning methods because radar sensors operate in all weather and lighting circumstances and have diverse image characteristics depending on the wavelength. Although many researchers have applied deep learning techniques to classify the land cover features, most of them need to do more by taking less or more several land cover features and taking proper evaluation metrics to justify the classification techniques. The current research shows segmentation on Sentinel-1 C-band synthetic aperture radar (SAR) images of Gujarat's Ahmedabad, Anand, and Kheda regions using four deep-learning models. U-Net, SegNet, FCN, and DeepLabV3+ have primarily been employed in prior research to classify SAR and other satellite image formats and showed excellent results in identifying different land cover features. Training samples of five primary classes for ground-truth data were obtained using Google Earth and Sentinel-2 images as reference maps. For segmenting the Sentinel-1 image, above mentioned four deep-learning semantic segmentation models were chosen and fine-tuned. All models did exceptionally well, with overall accuracy ranging from 76% to 90%. U-Net fared well, followed by DeepLabV3+ in classifying all five land cover features, with mean intersection over union (IoU) of 0.67 and 0.55, respectively. The results reveal that using a self-generated reference map, the Sentinel-1 dual-pol images can categorize the primary land cover features.

show abstract

Section: Data Annotation Approachesmentioning

confidence: 99%

Section: Classification Of Sar Images Using Deep Learning Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance Evaluation of DL-based Models for LULC segmentation using SAR time series data of regions in Gujarat

Jain

Varghese²,

Joshi

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Time-first approach for land cover mapping using big Earth observation data time-series in a data cube – a case study from the Lake Geneva region (Switzerland)

Giuliani

2024

Big Earth Data

View full text Add to dashboard Cite

A super-ensemble approach to map land cover types with high resolution over data-sparse African savanna landscapes

Cited by 2 publications

References 57 publications

Performance Evaluation of DL-based Models for LULC segmentation using SAR time series data of regions in Gujarat

Performance Evaluation of DL-based Models for LULC segmentation using SAR time series data of regions in Gujarat

Time-first approach for land cover mapping using big Earth observation data time-series in a data cube – a case study from the Lake Geneva region (Switzerland)

Contact Info

Product

Resources

About