From land cover-graphs to urban structure types

Walde, I.; Hese, S.; Berger, Christian; Schmullius, Christiane

doi:10.1080/13658816.2013.865189

Cited by 62 publications

(43 citation statements)

References 53 publications

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“…Another method involves object-based classification, in which a land cover map is first interpreted from high-spatial-resolution RSI. Then, spatial metrics, image texture, and nearest-neighbor relationships of land cover objects are adopted as parameters to identify urban functional zones [18,19]. Using the object-based classification method, Voltersen et al classified every block in Berlin (Germany) into specific land use types [12].…”

Section: Introductionmentioning

confidence: 99%

Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data

Jia

Ling

et al. 2018

Remote Sensing

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Land use is of great importance for urban planning, environmental monitoring, and transportation management. Several methods have been proposed to obtain land use maps of urban areas, and these can be classified into two categories: remote sensing methods and social sensing methods. However, remote sensing and social sensing approaches have specific disadvantages regarding the description of social and physical features, respectively. Therefore, an appropriate fusion strategy is vital for large-area land use mapping. To address this issue, we propose an efficient land use mapping method that combines remote sensing imagery (RSI) and mobile phone positioning data (MPPD) for large areas. We implemented this method in two steps. First, a support vector machine was adopted to classify the RSI and MPPD. Then, the two classification results were fused using a decision fusion strategy to generate the land use map. The proposed method was applied to a case study of the central area of Beijing. The experimental results show that the proposed method improved classification accuracy compared with that achieved using MPPD alone, validating the efficacy of this new approach for identifying land use. Based on the land use map and MPPD data, activity density in key zones during daytime and nighttime was analyzed to illustrate the volume and variation of people working and living across different regions.

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Section: Introductionmentioning

confidence: 99%

Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data

Jia

Ling

et al. 2018

Remote Sensing

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“…Graph-based-metrics describe the frequency of the local spatial arrangement of land cover elements within a land use object, e.g. based on an adjacency-event matrix (Barnsley & Barr, 1996;Walde et al, 2014). Recent work has focused on including context into the classification process by using context features (Hermosilla et al, 2012) and Markov or Conditional Random Fields (CRF) (Montanges et al, 2015;Novack and Stilla, 2015;Albert et al, 2017).…”

Section: Related Workmentioning

confidence: 99%

Classification of Land Cover and Land Use Based on Convolutional Neural Networks

Chun

Rottensteiner

Heipke

2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Land cover describes the physical material of the earth's surface, whereas land use describes the socio-economic function of a piece of land. Land use information is typically collected in geospatial databases. As such databases become outdated quickly, an automatic update process is required. This paper presents a new approach to determine land cover and to classify land use objects based on convolutional neural networks (CNN). The input data are aerial images and derived data such as digital surface models. Firstly, we apply a CNN to determine the land cover for each pixel of the input image. We compare different CNN structures, all of them based on an encoder-decoder structure for obtaining dense class predictions. Secondly, we propose a new CNN-based methodology for the prediction of the land use label of objects from a geospatial database. In this context, we present a strategy for generating image patches of identical size from the input data, which are classified by a CNN. Again, we compare different CNN architectures. Our experiments show that an overall accuracy of up to 85.7% and 77.4% can be achieved for land cover and land use, respectively. The classification of land cover has a positive contribution to the classification of the land use classification.

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“…Examples of these indexes computed for each profile are: normalized number of building segments, average length of building segments and standard deviation of the length of the building profile segments. Another way of describing the disposition of objects inside a block is by creating a network connecting neighboring features of the same or related types (e.g., same or related land cover class) and then extracting attributes from this network, as performed by Hermosillaa et al [29] and Walde et al [14]. Such a network is created usually by defining one node for each feature and then establishing edges between them based, for example, on the criteria of direct adjacency or distance thresholds.…”

Section: Descriptive Attributes For Classifying Urban Structure Typesmentioning

confidence: 99%

Classifying the Built-Up Structure of Urban Blocks with Probabilistic Graphical Models and TerraSAR-X Spotlight Imagery

Novack

Stilla

2018

Remote Sensing

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Up-to-date maps of a city's urban structure types (USTs) are very important for effective planning, as well as for different studies and applications. We present an approach for the classification of USTs at the level of urban blocks based on high-resolution spaceborne radar imagery. Images obtained at the satellite's ascending and descending orbits were used for extracting line and polygon features from each block. Most of the attributes considered in the classification concern the geometry of these features, as well as their spatial disposition inside the blocks. Furthermore, assuming the UST classes of neighboring blocks are probabilistically dependent, we explored the framework of probabilistic graphical models and propose different context-based classification models. These models differ with respect to (i) their type, i.e., Markov or conditional random fields, (ii) their parameterization and (iii) the criterion applied for establishing pairwise neighboring relationships between blocks. In our experiments, 1,695 blocks from the city of Munich (Germany) and five representative UST classes were considered. A standard classification performed with the Random Forest algorithm achieved an overall accuracy of nearly 70%. All context-based classifications achieved overall accuracies up to 7% higher than that. The results indicate that denser pairwise block-neighborhood structures lead to better results and that the accuracy improvement is higher when the strength of the contextual influences is proportional to the similarity of the neighboring blocks attributes.

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From land cover-graphs to urban structure types

Cited by 62 publications

References 53 publications

Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data

Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data

Classification of Land Cover and Land Use Based on Convolutional Neural Networks

Classifying the Built-Up Structure of Urban Blocks with Probabilistic Graphical Models and TerraSAR-X Spotlight Imagery

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