LAND USE CLASSIFICATION OF TOPOGRAPHICAL MAPS WITH DEEP LEARNING

2021 ◽  
Vol 77 (4) ◽  
pp. 400-411
Author(s):  
Toshiharu KOJIMA ◽  
Chantsal NARANTSETSEG ◽  
Keisuke OHASHI
Keyword(s):  
Land Use ◽  
Deep Learning ◽  
Land Use Classification ◽  
Topographical Maps

scholarly journals Multiview Deep Learning for Land-Use Classification

2015 ◽  
Vol 12 (12) ◽  
pp. 2448-2452 ◽  
Author(s):  
F. P. S. Luus ◽  
B. P. Salmon ◽  
F. van den Bergh ◽  
B. T. J. Maharaj
Keyword(s):  
Land Use ◽  
Deep Learning ◽  
Land Use Classification

LAND-USE CLASSIFICATION OF GRAY-SCALE AERIAL IMAGES USING PROBABILISTIC NEURAL NETWORKS

2004 ◽  
Vol 47 (5) ◽  
pp. 1813-1819 ◽  
Author(s):  
D. Ashish ◽  
G. Hoogenboom ◽  
R. W. McClendon
Keyword(s):  
Neural Networks ◽  
Land Use ◽  
Aerial Images ◽  
Land Use Classification ◽  
Probabilistic Neural Networks ◽  
Gray Scale

scholarly journals Land Use and Its Suitability to the Spatial Pattern in Batam City

2020 ◽  
Vol 4 (2) ◽  
pp. 363-366
Author(s):  
Novika Dora ◽  
Arif Roziqin
Keyword(s):  
Land Use ◽  
Spatial Pattern ◽  
City Government ◽  
Land Use Classification ◽  
Class Iii ◽  
Human Needs ◽  
Bare Land ◽  
The Government ◽  
Land Use Maps

Land use continues to grow as population increases in an area, various activities and human needs require land. Land use will affect the suitability of the spatial pattern determined by the Government stipulated in the laws and regulations governing spatial patterns. The purpose of this research is to identify land use that occurred in Batam City in 2019 and determine the suitability of the land use of the Batam City spatial pattern. In this study, the spatial pattern used is the spatial pattern obtained from BP Batam, this is because the spatial pattern originating from the Batam City Government has not yet been approved. The research method used is the method of Classification of Multispectral Maximum Likelihood and Overlay. The results of the map show the class of land use classifications totaling 11 classes in accordance with the class III land use classification class specified by Malingreau, which consists of lakes, forests, industry, pool, bare land, mangroves, ports, plantations, settlements, airports, and livestock. The results of the suitability of land use maps to the spatial pattern of Batam City indicate that the area of the area that is in accordance with the spatial pattern is 30986.77 Ha and the area that is not suitable is 34554.29 Ha.

scholarly journals AN ITERATIVE INFERENCE PROCEDURE APPLYING CONDITIONAL RANDOM FIELDS FOR SIMULTANEOUS CLASSIFICATION OF LAND COVER AND LAND USE

2015 ◽  
Vol II-3/W5 ◽  
pp. 369-376 ◽  
Author(s):  
L. Albert ◽  
F. Rottensteiner ◽  
C. Heipke
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Main Idea ◽  
Aerial Images ◽  
Land Use Classification ◽  
Inference Procedure ◽  
Cover Layer

Land cover and land use exhibit strong contextual dependencies. We propose a novel approach for the simultaneous classification of land cover and land use, where semantic and spatial context is considered. The image sites for land cover and land use classification form a hierarchy consisting of two layers: a <i>land cover layer</i> and a <i>land use layer</i>. We apply Conditional Random Fields (CRF) at both layers. The layers differ with respect to the image entities corresponding to the nodes, the employed features and the classes to be distinguished. In the land cover layer, the nodes represent super-pixels; in the land use layer, the nodes correspond to objects from a geospatial database. Both CRFs model spatial dependencies between neighbouring image sites. The complex semantic relations between land cover and land use are integrated in the classification process by using contextual features. We propose a new iterative inference procedure for the simultaneous classification of land cover and land use, in which the two classification tasks mutually influence each other. This helps to improve the classification accuracy for certain classes. The main idea of this approach is that semantic context helps to refine the class predictions, which, in turn, leads to more expressive context information. Thus, potentially wrong decisions can be reversed at later stages. The approach is designed for input data based on aerial images. Experiments are carried out on a test site to evaluate the performance of the proposed method. We show the effectiveness of the iterative inference procedure and demonstrate that a smaller size of the super-pixels has a positive influence on the classification result.

scholarly journals Understanding deep learning in land use classification based on Sentinel-2 time series

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Manuel Campos-Taberner ◽  
Francisco Javier García-Haro ◽  
Beatriz Martínez ◽  
Emma Izquierdo-Verdiguier ◽  
Clement Atzberger ◽  
...  
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Time Series ◽  
Deep Learning ◽  
Agricultural Policy ◽  
Near Infrared ◽  
Land Use Classification ◽  
Policy Compliance ◽  
Economic Return ◽  
Sentinel 2

Abstract The use of deep learning (DL) approaches for the analysis of remote sensing (RS) data is rapidly increasing. DL techniques have provided excellent results in applications ranging from parameter estimation to image classification and anomaly detection. Although the vast majority of studies report precision indicators, there is a lack of studies dealing with the interpretability of the predictions. This shortcoming hampers a wider adoption of DL approaches by a wider users community, as model’s decisions are not accountable. In applications that involve the management of public budgets or policy compliance, a better interpretability of predictions is strictly required. This work aims to deepen the understanding of a recurrent neural network for land use classification based on Sentinel-2 time series in the context of the European Common Agricultural Policy (CAP). This permits to address the relevance of predictors in the classification process leading to an improved understanding of the behaviour of the network. The conducted analysis demonstrates that the red and near infrared Sentinel-2 bands convey the most useful information. With respect to the temporal information, the features derived from summer acquisitions were the most influential. These results contribute to the understanding of models used for decision making in the CAP to accomplish the European Green Deal (EGD) designed in order to counteract climate change, to protect biodiversity and ecosystems, and to ensure a fair economic return for farmers.

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