scholarly journals Integration of Machine Learning and Open Access Geospatial Data for Land Cover Mapping

2019 ◽  
Vol 11 (16) ◽  
pp. 1907 ◽  
Author(s):  
Mohammad Mardani ◽  
Hossein Mardani ◽  
Lorenzo De Simone ◽  
Samuel Varas ◽  
Naoki Kita ◽  
...  
Keyword(s):  
Machine Learning ◽  
Developing Countries ◽  
Open Access ◽  
Land Cover ◽  
Satellite Images ◽  
National Level ◽  
Google Earth ◽  
Support Vector ◽  
Land Cover Mapping ◽  
Sustainable Food

In-time and accurate monitoring of land cover and land use are essential tools for countries to achieve sustainable food production. However, many developing countries are struggling to efficiently monitor land resources due to the lack of financial support and limited access to adequate technology. This study aims at offering a solution to fill in such a gap in developing countries, by developing a land cover solution that is free of costs. A fully automated framework for land cover mapping was developed using 10-m resolution open access satellite images and machine learning (ML) techniques for the African country of Lesotho. Sentinel-2 satellite images were accessed through Google Earth Engine (GEE) for initial processing and feature extraction at a national level. Also, Food and Agriculture Organization’s land cover of Lesotho (FAO LCL) data were used to train a support vector machine (SVM) and bagged trees (BT) classifiers. SVM successfully classified urban and agricultural lands with 62 and 67% accuracy, respectively. Also, BT could classify the two categories with 81 and 65% accuracy, correspondingly. The trained models could provide precise LC maps in minutes or hours. they can also be utilized as a viable solution for developing countries as an alternative to traditional geographic information system (GIS) methods, which are often labor intensive, require acquisition of very high-resolution commercial satellite imagery, time consuming and call for high budgets.

Synthetic sampling for spatio-temporal land cover mapping with machine learning and the Google Earth Engine in Andalusia, Spain

2020 ◽  
Author(s):  
Laura Bindereif ◽  
Tobias Rentschler ◽  
Martin Batelheim ◽  
Marta Díaz-Zorita Bonilla ◽  
Philipp Gries ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Land Cover ◽  
Classification Accuracy ◽  
Sampling Methods ◽  
Google Earth ◽  
Land Cover Mapping ◽  
Machine Learning Applications ◽  
Google Earth Engine ◽  
Spatio Temporal

<p>Land cover information plays an essential role for resource development, environmental monitoring and protection. Amongst other natural resources, soils and soil properties are strongly affected by land cover and land cover change, which can lead to soil degradation. Remote sensing techniques are very suitable for spatio-temporal mapping of land cover mapping and change detection. With remote sensing programs vast data archives were established. Machine learning applications provide appropriate algorithms to analyse such amounts of data efficiently and with accurate results. However, machine learning methods require specific sampling techniques and are usually made for balanced datasets with an even training sample frequency. Though, most real-world datasets are imbalanced and methods to reduce the imbalance of datasets with synthetic sampling are required. Synthetic sampling methods increase the number of samples in the minority class and/or decrease the number in the majority class to achieve higher model accuracy. The Synthetic Minority Over-Sampling Technique (SMOTE) is a method to generate synthetic samples and balance the dataset used in many machine learning applications. In the middle Guadalquivir basin, Andalusia, Spain, we used random forests with Landsat images from 1984 to 2018 as covariates to map the land cover change with the Google Earth Engine. The sampling design was based on stratified random sampling according to the CORINE land cover classification of 2012. The land cover classes in our study were arable land, permanent crops (plantations), pastures/grassland, forest and shrub. Artificial surfaces and water bodies were excluded from modelling. However, the number of the 130 training samples was imbalanced. The classes pasture (7 samples) and shrub (13 samples) show a lower number than the other classes (48, 47 and 16 samples). This led to misclassifications and negatively affected the classification accuracy. Therefore, we applied SMOTE to increase the number of samples and the classification accuracy of the model. Preliminary results are promising and show an increase of the classification accuracy, especially the accuracy of the previously underrepresented classes pasture and shrub. This corresponds to the results of studies with other objectives which also see the use of synthetic sampling methods as an improvement for the performance of classification frameworks.</p>

scholarly journals LAND COVER MAPPING USING SENTINEL-1 SAR DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 757-761 ◽  
Author(s):  
S. Abdikan ◽  
F. B. Sanli ◽  
M. Ustuner ◽  
F. Calò
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Google Earth ◽  
Support Vector ◽  
Land Cover Mapping ◽  
Large Cities ◽  
Sar Data ◽  
Sar Imagery ◽  
Urban Planning And Management ◽  
Land Cover Maps

In this paper, the potential of using free-of-charge Sentinel-1 Synthetic Aperture Radar (SAR) imagery for land cover mapping in urban areas is investigated. To this aim, we use dual-pol (VV+VH) Interferometric Wide swath mode (IW) data collected on September 16th 2015 along descending orbit over Istanbul megacity, Turkey. Data have been calibrated, terrain corrected, and filtered by a 5x5 kernel using gamma map approach. During terrain correction by using a 25m resolution SRTM DEM, SAR data has been resampled resulting into a pixel spacing of 20m. Support Vector Machines (SVM) method has been implemented as a supervised pixel based image classification to classify the dataset. During the classification, different scenarios have been applied to find out the performance of Sentinel-1 data. The training and test data have been collected from high resolution image of Google Earth. Different combinations of VV and VH polarizations have been analysed and the resulting classified images have been assessed using overall classification accuracy and Kappa coefficient. Results demonstrate that, combining opportunely dual polarization data, the overall accuracy increases up to 93.28% against 73.85% and 70.74% of using individual polarization VV and VH, respectively. Our preliminary analysis points out that dual polarimetric Sentinel-1SAR data can be effectively exploited for producing accurate land cover maps, with relevant advantages for urban planning and management of large cities.

Machine learning-based ship detection and tracking using satellite images for maritime surveillance

2021 ◽  
Vol 13 (5) ◽  
pp. 361-371
Author(s):  
Yu Wang ◽  
G. Rajesh ◽  
X. Mercilin Raajini ◽  
N. Kritika ◽  
A. Kavinkumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Satellite Images ◽  
Google Earth ◽  
Support Vector ◽  
Linear Discriminant ◽  
Ship Detection ◽  
Maritime Surveillance ◽  
Detection And Tracking ◽  
Multiple Hypothesis Tracking

The recent advancement in remote sensing technologies has resulted in the availability of different imaging modes and higher resolution satellite images. Accessibility of these remote sensing or satellite images, automatic ship detection and tracking has become an important research topic in the field of maritime surveillance. In this paper, a novel method for ship detection using satellite images is proposed. First the preprocessing is carried out to remove the noise from the images using Ship Detection and Tracking (SDT) filter. Then, the land masking (sea-land area separation) and cloud masking is carried out based on the gradient feature extraction using SDT edge detection, along with SDT segmentation. Finally, the ships are identified using the Machine Learning (ML) classifiers like Support Vector Machine (SVM), Random Forest Classifier (RFC), Linear Discriminant Analysis (LDA), Logistic Regression (LR), KNN, and Gaussian Naïve Bayes-based classifier based on the features extracted from Histogram of Oriented Gradients (HOG). The proposed work is cross validated using the Google earth data. Performance of our proposed method is evaluated using the recall and the precision values. Further, for tracking ships, an improved multiple hypothesis tracking (MHT) algorithm is proposed and tested using the Kaggle dataset.

scholarly journals LAND COVER MAPPING USING SENTINEL-1 SAR DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 757-761 ◽  
Author(s):  
S. Abdikan ◽  
F. B. Sanli ◽  
M. Ustuner ◽  
F. Calò
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Google Earth ◽  
Support Vector ◽  
Land Cover Mapping ◽  
Large Cities ◽  
Sar Data ◽  
Sar Imagery ◽  
Urban Planning And Management ◽  
Land Cover Maps

In this paper, the potential of using free-of-charge Sentinel-1 Synthetic Aperture Radar (SAR) imagery for land cover mapping in urban areas is investigated. To this aim, we use dual-pol (VV+VH) Interferometric Wide swath mode (IW) data collected on September 16th 2015 along descending orbit over Istanbul megacity, Turkey. Data have been calibrated, terrain corrected, and filtered by a 5x5 kernel using gamma map approach. During terrain correction by using a 25m resolution SRTM DEM, SAR data has been resampled resulting into a pixel spacing of 20m. Support Vector Machines (SVM) method has been implemented as a supervised pixel based image classification to classify the dataset. During the classification, different scenarios have been applied to find out the performance of Sentinel-1 data. The training and test data have been collected from high resolution image of Google Earth. Different combinations of VV and VH polarizations have been analysed and the resulting classified images have been assessed using overall classification accuracy and Kappa coefficient. Results demonstrate that, combining opportunely dual polarization data, the overall accuracy increases up to 93.28% against 73.85% and 70.74% of using individual polarization VV and VH, respectively. Our preliminary analysis points out that dual polarimetric Sentinel-1SAR data can be effectively exploited for producing accurate land cover maps, with relevant advantages for urban planning and management of large cities.

scholarly journals Spatio-Temporal Classification and Prediction of Land Use and Land Cover Change for the Vembanad Lake System, Kerala – a Machine Learning Approach

2021 ◽  
Author(s):  
Parthasarathy Kulithalai Shiyam Sundar ◽  
Paresh Chandra Deka
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Land Cover ◽  
Google Earth ◽  
Anthropogenic Activity ◽  
Support Vector ◽  
Learning Approaches ◽  
Barren Land ◽  
Lake System ◽  
Spatio Temporal

Abstract Land use and land cover (LULC) change has become a critical issue for decision planners and conservationists due to inappropriate growth and its effect on natural ecosystems. As a result, the goal of this study is to identify the LULC for the Vembanad Lake System (VLS), Kerala in the short term, i.e., within a decade, utilizing two standard machine learning approaches, Random Forest (RF) and Support Vector Machines (SVM), on the Google Earth Engine (GEE) platform. When comparing the two techniques, SVM is classified at an average accuracy of around 84.5%, while RF is classified at 89%. The RF outperformed the SVM in almost identical spectral classes such as barren land and built-up areas. As a result, RF classified LULC is considered to predict the Spatio-temporal distribution of LULC transition analysis for 2035 and 2050. The study was conducted in Idrisi TerrSet software using the Cellular Automata (CA)-Markov chain analysis. The model's efficiency is evaluated by comparing the projected 2019 image to the actual 2019 classified image. The efficiency was good with more than 94% accuracy for the classes except for barren land, which might have resulted from the recent natural calamities and the accelerated anthropogenic activity in the area.

scholarly journals A MACHINE LEARNING DATASET FOR LARGE-SCOPE HIGH RESOLUTION REMOTE SENSING IMAGE INTERPRETATION CONSIDERING LANDSCAPE SPATIAL HETEROGENEITY

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 731-736
Author(s):  
Y. Xu ◽  
X. Hu ◽  
Y. Wei ◽  
Y. Yang ◽  
D. Wang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Land Cover ◽  
Spatial Heterogeneity ◽  
Satellite Remote Sensing ◽  
Sampling Method ◽  
National Level ◽  
Remote Sensing Image ◽  
Training Sample ◽  
Large Scope

<p><strong>Abstract.</strong> The demand for timely information about earth’s surface such as land cover and land use (LC/LU), is consistently increasing. Machine learning method shows its advantage on collecting such information from remotely sensed images while requiring sufficient training sample. For satellite remote sensing image, however, sample datasets covering large scope are still limited. Most existing sample datasets for satellite remote sensing image built based on a few frames of image located on a local area. For large scope (national level) view, choosing a sufficient unbiased sampling method is crucial for constructing balanced training sample dataset. Dependable spatial sample locations considering spatial heterogeneity of land cover are needed for choosing sample images. This paper introduces an ongoing work on establishing a national scope sample dataset for high spatial-resolution satellite remote sensing image processing. Sample sites been chosen sufficiently using spatial sampling method, and divided sample patches been grouped using clustering method for further uses. The neural network model for road detection trained our dataset subset shows an increased performance on both completeness and accuracy, comparing to two widely used public dataset.</p>

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