scholarly journals CONTEXTUAL LAND USE CLASSIFICATION: HOW DETAILED CAN THE CLASS STRUCTURE BE?

2016 ◽  
Vol XLI-B4 ◽  
pp. 11-18
Author(s):  
L. Albert ◽  
F. Rottensteiner ◽  
C. Heipke
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Image Data ◽  
Maximum Level ◽  
Aerial Image ◽  
Contextual Knowledge ◽  
Land Use Classification ◽  
The Hierarchical Structure ◽  
Semantic Resolution ◽  
Different Characteristics

The goal of this paper is to investigate the maximum level of semantic resolution that can be achieved in an automated land use change detection process based on mono-temporal, multi-spectral, high-resolution aerial image data. For this purpose, we perform a step-wise refinement of the land use classes that follows the hierarchical structure of most object catalogues for land use databases. The investigation is based on our previous work for the simultaneous contextual classification of aerial imagery to determine land cover and land use. Land cover is determined at the level of small image segments. Land use classification is applied to objects from the geospatial database. Experiments are carried out on two test areas with different characteristics and are intended to evaluate the step-wise refinement of the land use classes empirically. The experiments show that a semantic resolution of ten classes still delivers acceptable results, where the accuracy of the results depends on the characteristics of the test areas used. Furthermore, we confirm that the incorporation of contextual knowledge, especially in the form of contextual features, is beneficial for land use classification.

scholarly journals CONTEXTUAL LAND USE CLASSIFICATION: HOW DETAILED CAN THE CLASS STRUCTURE BE?

2016 ◽  
Vol XLI-B4 ◽  
pp. 11-18
Author(s):  
L. Albert ◽  
F. Rottensteiner ◽  
C. Heipke
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Image Data ◽  
Maximum Level ◽  
Aerial Image ◽  
Contextual Knowledge ◽  
Land Use Classification ◽  
The Hierarchical Structure ◽  
Semantic Resolution ◽  
Different Characteristics

The goal of this paper is to investigate the maximum level of semantic resolution that can be achieved in an automated land use change detection process based on mono-temporal, multi-spectral, high-resolution aerial image data. For this purpose, we perform a step-wise refinement of the land use classes that follows the hierarchical structure of most object catalogues for land use databases. The investigation is based on our previous work for the simultaneous contextual classification of aerial imagery to determine land cover and land use. Land cover is determined at the level of small image segments. Land use classification is applied to objects from the geospatial database. Experiments are carried out on two test areas with different characteristics and are intended to evaluate the step-wise refinement of the land use classes empirically. The experiments show that a semantic resolution of ten classes still delivers acceptable results, where the accuracy of the results depends on the characteristics of the test areas used. Furthermore, we confirm that the incorporation of contextual knowledge, especially in the form of contextual features, is beneficial for land use classification.

scholarly journals Land Use Classification Using Conditional Random Fields for the Verification of Geospatial Databases

2014 ◽  
Vol II-4 ◽  
pp. 1-7 ◽  
Author(s):  
L. Albert ◽  
F. Rottensteiner ◽  
C. Heipke
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Random Fields ◽  
Large Scale ◽  
Conditional Random Fields ◽  
Land Cover Classification ◽  
Test Site ◽  
Aerial Images ◽  
Contextual Knowledge ◽  
Land Use Classification

Geospatial land use databases contain important information with high benefit for several users, especially when they provide a detailed description on parcel level. Due to many changes connected with a high effort of the update process, these large-scale land use maps become outdated quickly. This paper presents a two-step approach for the automatic verification of land use objects of a geospatial database using high-resolution aerial images. In the first step, a precise pixel-based land cover classification using spectral, textural and three-dimensional features is applied. In the second step, an object-based land use classification follows, which is based on features derived from the pixel-based land cover classification as well as geometrical, spectral and textural features. For both steps, the potential of the incorporation of contextual knowledge in the classification process is explored. For this purpose, we use Conditional Random Fields (CRF), which have proven to be a flexible, powerful framework for contextual classification in various applications in remote sensing. The results of the approach are evaluated on an urban test site and the influence of different features and models on the classification accuracy is analysed. It is shown that the use of CRF for the land cover classification yields an improved accuracy and smoother results compared to independent pixel-based approaches. The integration of contextual knowledge also has a remarkable positive effect on the results of the land use classification.

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.

scholarly journals The Use of Spot-HRV Data for the Mapping of the Land Cover (applied upon East-Mono, Central Togo)

Afrika Focus ◽  
1991 ◽  
Vol 7 (1) ◽  
Author(s):  
Beata Maria De Vliegher
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Image Interpretation ◽  
Remote Sensing Data ◽  
Image Data ◽  
Visual Image ◽  
Data Set ◽  
Land Cover Map ◽  
Colour Composite

The mapping of the land use in a tropical wet and dry area (East-Mono, Central Togo) is made using remote sensing data, recorded by the satellite SPOT. The negative, multispectral image data set has been transferred into positives by photographical means and afterwards enhanced using the diazo technique. The combination of the different diazo coloured images resulted in a false colour composite, being the basic document for the visual image interpretation. The image analysis, based upon differences in colour and texture, resulted in a photomorphic unit map. The use of a decision tree including the various image characteristics allowed the conversion of the photomorphic unit map into a land cover map. For this, six main land cover types could be differentiated resulting in 16 different classes of the final map. KEY WORDS :Remote sensing, SPOT, Multispectral view, Visual image interpre- tation, Mapping, Vegetation, Land use, Togo. 

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 Multidimensional Land-use Information for Local Planning and Land Resources Assessment in Indonesia: Classification Scheme for Information Extraction from High-Spatial Resolution Imagery

2019 ◽  
Vol 51 (2) ◽  
pp. 131
Author(s):  
Projo Danoedoro
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Imagery ◽  
Survey Methods ◽  
Evaluation Criteria ◽  
Relevant Information ◽  
Land Use Classification ◽  
Local Planning ◽  
Specific Content ◽  
Data Source

Suitable land-cover/land-use  information is rarely available in most developing countries, particularly when newness, accuracy, relevance, and compatibility are used as evaluation criteria.  In Indonesia, various institutions developed their own maps with considerable differences in classification schemes, data sources and scales, as well as in survey methods.  Redundant land-cover/land-use surveys of the same area are frequently carried out to ensure the data contains relevant information. To overcome this problem, a multidimensional land-use classification system was developed. The system uses satellite imagery as main data source, with a multi-dimensional approach to link  land-cover information to land-use-related categories.  The land-cover/land-use layers represent image-based land-cover (spectral), spatial, temporal, ecological and socio-economic dimensions.  The final land-cover/land-use database can be used to derive a map with  specific content relevant to particular planning tasks. Methods for mapping each dimension are described in this paper, with examples using Quickbird satellite imagery covering a small part the Semarang area, Indonesia.  The approaches and methods used in this study may be applied to other countries having characteristics similar to those of Indonesia

scholarly journals Object-oriented classification for land cover of North Thang Long Industrial area using Worldview-2 data

2021 ◽  
Vol 62 (1) ◽  
pp. 10-18
Author(s):  
Ha Thu Thi Le ◽  
Long Van Hoang ◽  
Trung Van Nguyen ◽  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Spatial Resolution ◽  
Spectral Characteristics ◽  
Object Oriented ◽  
Remote Sensing Data ◽  
Industrial Area ◽  
Classification Method ◽  
Land Use Classification ◽  
Kappa Index

Land cover/land use classification using high spatial resolution remote sensing data has the biggest challenge is how to distinguish object classes from different spectral values based on structures, shapes, and spatial elements. This paper focuses on the object-oriented classification method to extract artificial surface at industrial area by Worldview-2 data with a spatial resolution of 1.8 m. Extraction of 05 types of land cover/land use using object-oriented classification method based on reflectance spectral characteristics, shape index, location of objects, brightness, NDVI index, and density objects are archive efficiency to the quality of classification results. The overall accuracy of classification result for land cover/land use of Thang Long industrial area is about 0.85 and Kappa index is about 0.81.

scholarly journals POI Mining for Land Use Classification: A Case Study

2020 ◽  
Vol 9 (9) ◽  
pp. 493 ◽  
Author(s):  
Renato Andrade ◽  
Ana Alves ◽  
Carlos Bento
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Areas ◽  
Smart Cities ◽  
Ground Truth ◽  
Land Use Classification ◽  
Data Types ◽  
Ground Truth Data ◽  
Functional Roles ◽  
Artificial Neural Network Ann

The modern planning and management of urban spaces is an essential topic for smart cities and depends on up-to-date and reliable information on land use and the functional roles of the places that integrate urban areas. In the last few years, driven by the increased availability of geo-referenced data from social media, embedded sensors, and remote sensing images, various techniques have become popular for land use analysis. In this paper, we first highlight and discuss the different data types and methods usually adopted in this context, as well as their purposes. Then, based on a systematic state-of-the-art study, we focused on exploring the potential of points of interest (POIs) for land use classification, as one of the most common categories of crowdsourced data. We developed an application to automatically collect POIs for the study area, creating a dataset that was used to generate a large number of features. We used a ranking technique to select, among them, the most suitable features for classifying land use. As ground truth data, we used CORINE Land Cover (CLC), which is a solid and reliable dataset available for the whole European territory. It was used an artificial neural network (ANN) in different scenarios and our results reveal values of more than 90% for the accuracy and F-score in one experiment performed. Our analysis suggests that POI data have promising potential to characterize geographic spaces. The work described here aims to provide an alternative to the current methodologies for land use and land cover (LULC) classification, which are usually time-consuming and depend on expensive data types.

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