Built-up Area Extraction Research Articles

Automatic Extraction of Built-Up Areas From Panchromatic and Multispectral Remote Sensing Images Using Double-Stream Deep Convolutional Neural Networks

As the central area of human activities, built-up area has been one of the most important objects that are recognized from a remote sensing image. Built-up area in different regions has characteristics as follows: the structure and texture of the built-up area are complex and diverse; the buildings have multitudinous materials; the vegetation distribution and background around the built-up area are changeable. The existing built-up area detection methods still face the challenge to achieve favorable precision and generalization ability. In this paper, a double-stream convolutional neural network (DSCNN) model is proposed to extract the built-up area automatically, which can combine the complementary cues of high-resolution panchromatic and multispectral image. Some postprocessing steps are adopted to make the results more reasonable. We manually annotated a large-scale dataset for training and testing DSCNN. Experiments demonstrate that the proposed method has a higher overall accuracy as well as better generalization ability compared to the state-of-the-art techniques.

EXTRACTION OF BUILT-UP AREA USING HIGH RESOLUTION SENTINEL-2A AND GOOGLE SATELLITE IMAGERY

Abstract. Accurate information about the built-up area in a city or town is essential for urban planners for proper planning of urban infrastructure facilities and other basic amenities. The normalized difference indices available in literature for built-up area extraction are mostly based on moderate resolution images such as Landsat Thematic Mapper (TM) and enhanced TM (ETM+) and may not be applicable for high resolution images such as Sentinel-2A. In the present study, an attempt has been made to extract the built-up area from Sentinel-2A satellite data of Chennai, India using normalized difference index (NDI) with different band combinations. It was found that the built-up area was clearly distinguishable when the index value ranges between −0.29 and −0.09 in blue and near-infrared (NIR) band combination. Post extraction editing using Google satellite imagery was also attempted to improve the extraction results. The results showed an overall accuracy of 90% and Kappa value of 0.785. Same approach when applied for another area also yields good results with overall accuracy of 92% and Kappa value of 0.83. As the proposed approach is simple to understand, yields accurate results and requires only open source data, the same can be used for extracting the built-up area using Sentinel-2A and Google satellite imagery.

A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery

Extracting built-up areas from remote sensing data like Landsat 8 satellite is a challenge. We have investigated it by proposing a new index referred as built-up land features extraction index (BLFEI). The BLFEI index takes advantage of its simplicity and good separability between the four major component of urban system, namely built-up, barren, vegetation and water. The histogram overlap method and the spectral discrimination index (SDI) are used to study separability. BLFEI index uses the two bands of infrared shortwaves, the red and green bands of the visible spectrum. OLI imagery of Algiers, Algeria, was used to extract built-up areas through BLFEI and some new previously developed built-up indices used for comparison. The water areas are masked out leading to Otsu’s thresholding algorithm to automatically find the optimal value for extracting built-up land from waterless regions. BLFEI, the new index improved the separability by 25% and the accuracy by 5%.

Building Detection from VHR Remote Sensing Imagery Based on the Morphological Building Index

Automatic detection of buildings from very high resolution (VHR) satellite images is a current research hotspot in remote sensing and computer vision. However, many irrelevant objects with similar spectral characteristics to buildings will cause a large amount of interference to the detection of buildings, thus making the accurate detection of buildings still a challenging task, especially for images captured in complex environments. Therefore, it is crucial to develop a method that can effectively eliminate these interferences and accurately detect buildings from complex image scenes. To this end, a new building detection method based on the morphological building index (MBI) is proposed in this study. First, the local feature points are detected from the VHR remote sensing imagery and they are optimized by the saliency index proposed in this study. Second, a voting matrix is calculated based on these optimized local feature points to extract built-up areas. Finally, buildings are detected from the extracted built-up areas using the MBI algorithm. Experiments confirm that our proposed method can effectively and accurately detect buildings in VHR remote sensing images captured in complex environments.

Urban Built-Up Area Extraction From Log- Transformed NPP-VIIRS Nighttime Light Composite Data

Accurate information on urban areas at regional and global scales is required for various socioeconomic and environmental applications. The nighttime light (NTL) composite data have proven to be an effective data source for extracting urban areas. Various urban mapping methods have been proposed in the literature to extract urban built-up areas from the Defense Meteorological Satellite Program’s Operational Linescan System NTL data with a variable accuracy. However, most of the previous methods cannot be directly applied to the NTL data derived from the Suomi National Polar-orbiting Partnership Satellite with the Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) sensor onboard. In this letter, we introduced a logarithmic transformation to preprocess the NPP-VIIRS NTL composite data. Then, four popular methods for urban built-up area extraction were tested using the original and log-transformed NTL data, respectively. The selected methods included the thresholding technique, Sobel-based edge detection, neighborhood statistics analysis, and watershed segmentation. The accuracy of the results was evaluated through validating the urban areas derived using each method against the referenced urban areas obtained from the National Land Cover Database for the U.S.. The results indicated that logarithmic transformation is an effective procedure for enhancing the difference between urban built-up areas and nonurban areas. The selected methods for urban built-up area extraction were found to perform better on the log-transformed NTL data than the original NTL data.

How green was my valley: forest canopy density in relation to topography and anthropogenic effects in Manipur valley, India

ABSTRACTForest canopy density (FCD) is a major factor in the evaluation of forest status and is an important indicator of possible management interventions. The study uses the FCD model with Landsat TM and Landsat 8 OLI images to assess canopy density in India’s Manipur valley and surrounding hills. Normalized difference built-up index (NDBI) was used to extract built-up areas and population density was retrieved from LandScan data, while elevation and slope were obtained from ASTER DEMs (30 m). Four types of canopy density were delineated with crown cover above 71%, 41–70%, 11–40%, below 10% and areas with no canopy cover, that is 0%. A sharp decline in forest area occurred during 1989–2016 at a rate of loss of 2.9 % year-1 with an average rate of deforestation of 3051 ha year-1. Dense forests exhibited remarkable degradation, especially towards the central valley. The variation in the topographical (elevation and slope) gradient resulted in significant differences in the canopy density over the study area barring some hill slopes. Population pressure and various developmental activities in recent decades led to forest degradation in this fragile yet rich Eastern Himalaya biodiversity hotspot.

A Multisource Remotely Sensed Data Oriented Method for “Ghost City” Phenomenon Identification

The rapid urbanization in China has corresponded with rapid economic development and beneficial transformations of society that have also exacerbated many sensitive urban problems. Several “ghost cities” have emerged due to the mismatch between the speed of expansion and the rationality of urban planning, especially when the extensive city construction goes far beyond the actual peoples’ demands. This paper presents a novel method for investigating “ghost city” phenomenon based on multisource remotely sensed data. Three components are included. First, coarse-to-fine built-up area extraction and multiscale integration based on nighttime light Defense Meteorological Satellite Program operational linescan system imagery, Chinese land cover/use datasets and maximum green vegetation fraction product. Second, improved “ghost city” index (IGCI) construction and discrimination of “ghost city” phenomenon comprising the rate of land urbanization, the rate of population urbanization, and the intensity of nighttime light. Third, comparison and validation of the IGCI with the traditional “ghost city” index with respect to spatio-temporal distribution and the landscape pattern of “ghost city.” The proposed method was validated using entire districts in Ordos, China. Experimental results demonstrated that the IGCI takes advantages of the spatio-temporal variations of cities and the characteristics of “ghost city” phenomenon. This is also validated by means of the per capita housing construction area and the landscape expansion index, and showed that the urban construction land grows much faster than the population of Ordos. With respect to the accuracy and distribution of spatial patterns, the obtained experimental results exhibit good consistency with results from previous statistical studies.

Operational Built-Up Areas Extraction for Cities in China Using Sentinel-1 SAR Data

To obtain accurate information in a timely manner on built-up areas (BAs) is essential for urban planning and natural hazard (e.g., earthquakes) response strategies. In this paper, a new method for BAs extraction using the Sentinel-1 SAR is proposed, which includes two steps: (1) Candidate BAs are first selected as seeds from images that show high backscattering and obvious textural patterns, as characterized by image intensity, Getis-Ord index, and the variogram texture features; (2) region growing is iteratively implemented from these seed pixels to extract the BAs. Sentinel-1 data, with 5 × 20 m2 resolution, are selected over eight cities with various environmental settings around China, to validate the robustness of the proposed method. The results show that the proposed method achieves higher detection accuracy and fewer commission errors compared with the intensity-based region growing and thresholding methods. An averaged accuracy of 96.5% in validation points of eight cities was achieved, which outperforms the GlobCover urban product in both urban and rural area, while fewer commission errors were achieved compared to Landsat data-based methods. Moreover, two polarizations (VV/VH) and the averaged channel are compared for BAs extraction in areas with various environments. It turns out that improved results can be achieved using the averaged image of two polarizations in north China, while the VV image is better suited for BAs extraction in south. These findings indicate that operational BAs mapping over China, and even globally, is possible, since the Sentinel-1 data can provide images with global coverage.

AUTOMATIC EXTRACTION OF URBAN BUILT-UP AREA BASED ON OBJECT-ORIENTED METHOD AND REMOTE SENSING DATA

Abstract. Built-up area marks the use of city construction land in the different periods of the development, the accurate extraction is the key to the studies of the changes of urban expansion. This paper studies the technology of automatic extraction of urban built-up area based on object-oriented method and remote sensing data, and realizes the automatic extraction of the main built-up area of the city, which saves the manpower cost greatly. First, the extraction of construction land based on object-oriented method, the main technical steps include: (1) Multi-resolution segmentation; (2) Feature Construction and Selection; (3) Information Extraction of Construction Land Based on Rule Set, The characteristic parameters used in the rule set mainly include the mean of the red band (Mean R), Normalized Difference Vegetation Index (NDVI), Ratio of residential index (RRI), Blue band mean (Mean B), Through the combination of the above characteristic parameters, the construction site information can be extracted. Based on the degree of adaptability, distance and area of the object domain, the urban built-up area can be quickly and accurately defined from the construction land information without depending on other data and expert knowledge to achieve the automatic extraction of the urban built-up area. In this paper, Beijing city as an experimental area for the technical methods of the experiment, the results show that: the city built-up area to achieve automatic extraction, boundary accuracy of 2359.65 m to meet the requirements. The automatic extraction of urban built-up area has strong practicality and can be applied to the monitoring of the change of the main built-up area of city.

EXTRACTION OF BUILT-UP AREAS USING CONVOLUTIONAL NEURAL NETWORKS AND TRANSFER LEARNING FROM SENTINEL-2 SATELLITE IMAGES

Abstract. With rapid globalization, the extent of built-up areas is continuously increasing. Extraction of features for classifying built-up areas that are more robust and abstract is a leading research topic from past many years. Although, various studies have been carried out where spatial information along with spectral features has been utilized to enhance the accuracy of classification. Still, these feature extraction techniques require a large number of user-specific parameters and generally application specific. On the other hand, recently introduced Deep Learning (DL) techniques requires less number of parameters to represent more abstract aspects of the data without any manual effort. Since, it is difficult to acquire high-resolution datasets for applications that require large scale monitoring of areas. Therefore, in this study Sentinel-2 image has been used for built-up areas extraction. In this work, pre-trained Convolutional Neural Networks (ConvNets) i.e. Inception v3 and VGGNet are employed for transfer learning. Since these networks are trained on generic images of ImageNet dataset which are having very different characteristics from satellite images. Therefore, weights of networks are fine-tuned using data derived from Sentinel-2 images. To compare the accuracies with existing shallow networks, two state of art classifiers i.e. Gaussian Support Vector Machine (SVM) and Back-Propagation Neural Network (BP-NN) are also implemented. Both SVM and BP-NN gives 84.31 % and 82.86 % overall accuracies respectively. Inception-v3 and VGGNet gives 89.43 % of overall accuracy using fine-tuned VGGNet and 92.10 % when using Inception-v3. The results indicate high accuracy of proposed fine-tuned ConvNets on a 4-channel Sentinel-2 dataset for built-up area extraction.

Spatio-temporal analysis of urban growth in selected small, medium and large Indian cities

This study attempts to ascertain the spatial and temporal variations in the evolution of Indian cities using multi-date remote sensing data. A two-stage object-based nearest neighbour classification approach with hierarchical segmentation was used to extract built-up area in selected small, medium and large cities, whereas object-based temporal inversion was applied for change analysis. The temporal trend of net population density, degree of scattered development and compactness of urban core in each city was determined using the built-up area. The study observed a declining trend in growth rate of built-up area in small and medium sized-cities, in contrast to large cities. However, the net population density in cities of all types is decreasing as urban growth has outpaced the corresponding population growth. Furthermore, small and medium cities indicated greater tendency for scattered development in comparison to large cities, whereas the core urban areas of the later appeared relatively less compact.

Multi-sensor feature fusion for very high spatial resolution built-up area extraction in temporary settlements

Detailed and up-to-date knowledge on the situation in temporary settlements of forced migrants plays an important role for effective humanitarian assistance. These settlements emerge as planned or spontaneous camps or camp-like structures, characterized by a small-scale physical morphology and high dynamics. Information on the built-up area (BUA; i.e. areas occupied by buildings) in these settlements provides important evidence on the local situation. The objective of this work is to present a generic procedure for the detailed extraction of BUA in complex temporary settlements from very high spatial resolution satellite data collected by different sensor types. The proposed approach is embedded in the methodological framework of object-based image analysis and is compound of i) the computation of an exhaustive set of spectral-spatial features aggregated on multiple hierarchic segmentation scales, ii) filter based feature subset selection and iii) supervised classification using a Random Forest classifier. Experimental results are obtained based on Pléiades multispectral optical and TerraSAR-X Staring Spotlight Synthetic Aperture Radar satellite imagery for six distinct but representative test areas within the refugee camp Al Zaatari in Jordan. The experiments include a detailed assessment of classification accuracy for varying configurations of considered feature types and training data set sizes as well as an analysis of the feature selection (FS) outcomes. We observe that the classification accuracy can be improved by the use of multiple segmentation levels as well as the integration of multi-sensor information and different feature types. In addition, the results show the potential of the applied FS approach for the identification of most relevant features. Accuracy values beyond 80% in terms of κ statistic and True Skill Statistic based on significantly reduced feature sets compared to the input underline the potential of the proposed method.

Automatic newly increased built-up area extraction from high-resolution remote sensing images using line-density-based visual saliency and PanTex

Feature-based change detection technologies using multitemporal remote sensing images are widely applied to find newly increased built-up areas (NIBUA) during the period of observation. This paper proposes an automatic object-based NIBUA extraction method using high-resolution remote sensing images, which is based on the integration of spectrum feature, edge-derived line-density-based visual saliency (LDVS) feature, and texture-derived built-up presence index (PanTex) feature. In the proposed method, image segmentation is first employed to obtain objects as basic units of detection. Next, due to the complexity of built-up areas in high-resolution images, LDVS images and PanTex images are produced for each temporal image, respectively. Then, to highlight built-up areas in complex scenes, a comprehensive measure for each object is calculated by integrating the newly increased measures from spectrum, LDVS, and PanTex features via a manner of Dempster–Shafer evidence fusion. Finally, the object-based NIBUA can be extracted by conducting binarization on the newly increased fused measure image. Comparison studies and experimental results demonstrate that our method can achieve a robust extraction of NIBUA from high-resolution remote sensing images with a higher detection accuracy. We conclude that this automatic way can play a positive role in reducing the artificial workload of the interpreters and the cost of monitoring a large-region area. It is encouraged to employ this method in a variety of applications, such as illegal construction land monitoring, land use/cover map update, and city planning.

Spatio temporal analysis of urban expansion and its impact on land use land cover: A case study of Guwahati metropolitan area.

This paper presents temporal and spatial dynamics of urban expansion and its impact on the land use land cover (LULC) in Guwahati Metropolitan Area, India by using Geographical Information Systems (GIS) and Remote Sensing. The LULC changes were detected through the integration of remotely sensed Landsat imageries and GIS for the year 1991 and 2016. Normalized Difference Built-up Index (NDBI) was implemented for automated extraction of built-up areas and maximum Likelihood Supervised Classification and post-classification techniques were carried out to examine the LULC changes over a period of 25 years. The growth trend of built-up area and the LULC changes show that there are substantial exchanges of areas between vegetation and built up land along with dense forest loss occurring mainly in the hills and foothills in and around Guwahati mainly due to the need for coming up with a substantial increase in population and economic activities. The limited land availability and the confinement of forest areas to the hills have forced the utilization of higher grounds resulting in huge land use transformation from vegetated land and forests to built-up land.

Step-wise Land-class Elimination Approach for extracting mixed-type built-up areas of Kolkata megacity

The extraction of urban built-up areas is an important aspect of urban planning and understanding the complex drivers and biophysical mechanism of urban climate processes. However, built-up area extraction using Landsat data is a challenging task due to spatio-temporal dynamics and spatially intermixed nature of Land Use and Land Cover (LULC) in the cities of the developing countries, particularly in tropics. In the light of advantages and drawbacks of the Normalized Difference Built-up Index (NDBI) and Built-up Area Extraction Method (BAEM), a new and simple method i.e. Step-wise Land-class Elimination Approach (SLEA) is proposed for rapid and accurate mapping of urban built-up areas without depending exclusively on the band specific normalized indices, in order to pursue a more generalized approach. It combines the use of a single band layer, Normalized Difference Vegetation Index (NDVI) image and another binary image obtained through Logit model. Based on the spectral designation of the satellite image in use, a particular band is chosen for identification of water pixels. The Double-window Flexible Pace Search (DFPS) approach is employed for finding the optimum threshold value that segments the selected band image into water and non-water categories. The water pixels are then eliminated from the original image. The vegetation pixels are similarly identified using the NDVI image and eliminated. The residual pixels left after elimination of water and vegetation categories belong either to the built-up areas or to bare land categories. Logit model is used for separation of the built-up areas from bare lands. The effectiveness of this method was tested through the mapping of built-up areas of the Kolkata Metropolitan Area (KMA), India from Thematic Mapper (TM) images of 2000, 2005 and 2010, and Operational Land Imager (OLI) image of 2015. Results of the proposed SLEA were 95.33% accurate on the whole, while those derived by the NDBI and BAEM approaches returned an overall accuracy of 83.67% and 89.33%, respectively. Comparisons of the results obtained using this method with those obtained from NDBI and BAEM approaches demonstrate that the proposed approach is quite reliable. The SLEA generates new patterns of evidence and hypotheses for built-up areas extraction research, providing an integral link with statistical science and encouraging trans-disciplinary collaborations to build robust knowledge and problem solving capacity in urban areas. It also brings landscape architecture, urban and regional planning, landscape and ecological engineering, and other practice-oriented fields to bear together in processes for identifying problems and analyzing, synthesizng, and evaluating desirable alternatives for urban change. This method produced very accurate results in a more efficient manner compared to the earlier built-up area extraction approaches for the landscape and urban planning.

Big earth data analytics on Sentinel-1 and Landsat imagery in support to global human settlements mapping

Continuous global-scale mapping of human settlements in the service of international agreements calls for massive volume of multi-source, multi-temporal, and multi-scale earth observation data. In this paper, the latest developments in terms of processing big earth observation data for the purpose of improving the Global Human Settlement Layer (GHSL) data are presented. Two experiments with Sentinel-1 and Landsat data collections were run leveraging on the Joint Research Centre Earth Observation Data and Processing Platform. A comparative analysis of the results of built-up areas extraction from different remote sensing data and processing workflows shows how the information production supported by data-intensive computing infrastructure for optimization and multiple testing can improve the output information reliability and consistency within the GHSL scope. The paper presents the processing workflows and the results of the two main experiments, giving insights into the enhanced mapping capabilities gained by analyzing Sentinel-1 and Landsat data-sets, and the lessons learnt in terms of handling and processing big earth observation data.

Automated Detection of Buildings from Heterogeneous VHR Satellite Images for Rapid Response to Natural Disasters

In this paper, we present a novel approach for automatically detecting buildings from multiple heterogeneous and uncalibrated very high-resolution (VHR) satellite images for a rapid response to natural disasters. In the proposed method, a simple and efficient visual attention method is first used to extract built-up area candidates (BACs) from each multispectral (MS) satellite image. After this, morphological building indices (MBIs) are extracted from all the masked panchromatic (PAN) and MS images with BACs to characterize the structural features of buildings. Finally, buildings are automatically detected in a hierarchical probabilistic model by fusing the MBI and masked PAN images. The experimental results show that the proposed method is comparable to supervised classification methods in terms of recall, precision and F-value.

A Strategy of Rapid Extraction of Built-Up Area Using Multi-Seasonal Landsat-8 Thermal Infrared Band 10 Images

Recently, studies have focused more attention on surface feature extraction using thermal infrared remote sensing (TIRS) as supplementary materials. Innovatively, in this paper, using three-date (winter, early spring, and end of spring) TIRS Band 10 images of Landsat-8, we proposed an empirical normalized difference of a seasonal brightness temperature index (NDSTI) for enhancing a built-up area based on the contrast heat emission seasonal response of a built-up area to solar radiation, and adopted a decision tree classification method for the rapidly accurate extraction of the built-up area. Four study areas, including one major experimental study area (Tangshan) and three verification areas (Minqin, Laizhou, and Yugan) in different climate zones, respectively, were used to empirically establish the overall strategy system, then we specified constrained conditions of this strategy. Moreover, we compared the NDSTI to the current built-up indices, respectively, for extracting the built-up area. The results showed that (1) the new index (NDSTI) exploited the seasonal thermal characteristic variation between the built-up area and other covers in the time series analysis, helping achieve more accurate built-up area extraction than other spectral indices; (2) this strategy could effectively realize rapid built-up area extraction with generally satisfied overall accuracy (over 80%), and was especially excellent in Tangshan and Laizhou; however, (3) it may be constrained by climate patterns and other surface characteristics, which need to be improved from the view of the results of Minqin and Yugan. In summary, the method developed in this study has the potential and advantage to extract the built-up area rapidly from the multi-seasonal thermal infrared remote sensing data. It could be an operative tool for long-term monitoring of built-up areas efficiently and for more applications of thermal infrared images in the future.

Enhanced automatic detection of human settlements using Sentinel-1 interferometric coherence

ABSTRACTThis article presents an experiment in which multi-temporal interferometric coherence calculated from 6-days Sentinel-1A and Sentinel-1B image pairs and backscatter intensity σ° are jointly used for the extraction of built-up areas in the framework of the symbolic machine learning classification. The results obtained with the proposed approach confirm the enhanced capabilities of discriminating built-up areas when using coherence information in comparison to two available global human settlement layers derived: (1) from Landsat optical data and (2) from Sentinel-1 ground range detected data and based on backscatter intensity σ° only. The experiment carried out in The Netherlands Randstad area is expected to be indicative of the results obtainable for urban areas having similar structures and types of built-up.

BUILT-UP AREA DETECTION BASED ON SUBSPACE PROJECTIONS USING POLARIMETRIC SAR DATA

Abstract. The task of detecting and identifying objects remotely has long been an area of intense interest and active research. Active sensing of objects with radio waves is a whole new domain of target detection which is made available by radar remote sensors. Land cover/use information extraction is one of the most important applications of radar remote sensing, especially in urban areas. In this paper, we take a new look at the built-up area extraction problem in polarimetric SAR (PolSAR) data and assume canonical scattering mechanisms as our signal sources which combination of them with appropriate weight fractions formed a scattering vector of each pixel. The set of the scattering mechanisms is divided into two groups: the scattering mechanism of built-up area, and non-objected scattering mechanisms. Then, we describe a technique which simultaneously annihilates the effect of non-objected scattering mechanisms, and detects the presence of a scattering mechanism of interest. The experimental results on several quad-polarimetric datasets show the significant agreement with expected results, while saving computational complexity.