Reverse Engineering of Land Cover Data: Machine Learning for Data Replication in the Spatial and Temporal Domains

2017 ◽  
pp. 3-22
Author(s):  
Galen Maclaurin ◽  
Stefan Leyk
Keyword(s):  
Machine Learning ◽  
Land Cover ◽  
Reverse Engineering ◽  
Data Replication ◽  
Land Cover Data

Chesapeake bay watershed land cover data series

Data Series ◽  
10.3133/ds505 ◽  
2010 ◽  
Author(s):  
Frederick M. Irani ◽  
Peter R. Claggett
Keyword(s):  
Land Cover ◽  
Chesapeake Bay ◽  
Data Series ◽  
Chesapeake Bay Watershed ◽  
Land Cover Data

Automatic Selection of Classified Samples with the Help of Previous Land Cover Data

2012 ◽  
Vol 14 (4) ◽  
pp. 507-513
Author(s):  
Kun LIU ◽  
Xiaomei YANG ◽  
Tao ZHANG
Keyword(s):  
Land Cover ◽  
Automatic Selection ◽  
Land Cover Data ◽  
Selection Of

scholarly journals A Comparative Analysis of Spatial Data and Land Use/Land Cover Classification in Urbanized Areas and Areas Subjected to Anthropogenic Pressure for the Example of Poland

2021 ◽  
Vol 13 (6) ◽  
pp. 3070
Author(s):  
Patrycja Szarek-Iwaniuk
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Classification ◽  
Classification Systems ◽  
Anthropogenic Pressure ◽  
Land Use Land Cover ◽  
Land Use Classification ◽  
Land Cover Data ◽  
Spatial Changes ◽  
Land Use Types

Urbanization processes are some of the key drivers of spatial changes which shape and influence land use and land cover. The aim of sustainable land use policies is to preserve and manage existing resources for present and future generations. Increasing access to information about land use and land cover has led to the emergence of new sources of data and various classification systems for evaluating land use and spatial changes. A single globally recognized land use classification system has not been developed to date, and various sources of land-use/land-cover data exist around the world. As a result, data from different systems may be difficult to interpret and evaluate in comparative analyses. The aims of this study were to compare land-use/land-cover data and selected land use classification systems, and to determine the influence of selected classification systems and spatial datasets on analyses of land-use structure in the examined area. The results of the study provide information about the existing land-use/land-cover databases, revealing that spatial databases and land use and land cover classification systems contain many equivalent land-use types, but also differ in various respects, such as the level of detail, data validity, availability, number of land-use types, and the applied nomenclature.

Changing weather and climate in Northern Ghana: comparison of local perceptions with meteorological and land cover data

2016 ◽  
Vol 17 (3) ◽  
pp. 915-928 ◽  
Author(s):  
Katherine L. Dickinson ◽  
Andrew J. Monaghan ◽  
Isaac J. Rivera ◽  
Leiqiu Hu ◽  
Ernest Kanyomse ◽  
...  
Keyword(s):  
Land Cover ◽  
Northern Ghana ◽  
Local Perceptions ◽  
Land Cover Data ◽  
Weather And Climate

scholarly journals Geohazards Susceptibility Assessment along the Upper Indus Basin Using Four Machine Learning and Statistical Models

2021 ◽  
Vol 10 (5) ◽  
pp. 315
Author(s):  
Hilal Ahmad ◽  
Chen Ningsheng ◽  
Mahfuzur Rahman ◽  
Md Monirul Islam ◽  
Hamid Reza Pourghasemi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Cover ◽  
Debris Flows ◽  
Regional Scale ◽  
Indus Basin ◽  
Annual Rainfall ◽  
Slope Aspect ◽  
Topographic Wetness Index ◽  
Susceptibility Maps ◽  
Landslides And Debris Flows

The China–Pakistan Economic Corridor (CPEC) project passes through the Karakoram Highway in northern Pakistan, which is one of the most hazardous regions of the world. The most common hazards in this region are landslides and debris flows, which result in loss of life and severe infrastructure damage every year. This study assessed geohazards (landslides and debris flows) and developed susceptibility maps by considering four standalone machine-learning and statistical approaches, namely, Logistic Regression (LR), Shannon Entropy (SE), Weights-of-Evidence (WoE), and Frequency Ratio (FR) models. To this end, geohazard inventories were prepared using remote sensing techniques with field observations and historical hazard datasets. The spatial relationship of thirteen conditioning factors, namely, slope (degree), distance to faults, geology, elevation, distance to rivers, slope aspect, distance to road, annual mean rainfall, normalized difference vegetation index, profile curvature, stream power index, topographic wetness index, and land cover, with hazard distribution was analyzed. The results showed that faults, slope angles, elevation, lithology, land cover, and mean annual rainfall play a key role in controlling the spatial distribution of geohazards in the study area. The final susceptibility maps were validated against ground truth points and by plotting Area Under the Receiver Operating Characteristic (AUROC) curves. According to the AUROC curves, the success rates of the LR, WoE, FR, and SE models were 85.30%, 76.00, 74.60%, and 71.40%, and their prediction rates were 83.10%, 75.00%, 73.50%, and 70.10%, respectively; these values show higher performance of LR over the other three models. Furthermore, 11.19%, 9.24%, 10.18%, 39.14%, and 30.25% of the areas corresponded to classes of very-high, high, moderate, low, and very-low susceptibility, respectively. The developed geohazard susceptibility map can be used by relevant government officials for the smooth implementation of the CPEC project at the regional scale.

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