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Urban rail transit stations are the key nodes of urban rail transit network. Identifying and analyzing land use characteristics around urban rail transit stations can significantly contribute to urban rail transportation operation and management. Therefore, a visualization method of land use characteristics around urban rail transit stations based on POI is proposed in this paper. In the proposed method, first, the Voronoi diagram is used to determine coverage of urban rail transit stations and each POI is put in a coverage area based on their physical location. Then, topic-oriented hierarchical POIs of each urban rail transit station are extracted based on skyline idea. Finally, the land use characteristics around an urban rail transit station are visualized based on the extracted hierarchical POIs. We carried out two case studies and a quality evaluation. By using realistic data from Beijing rail transit in order to validate the method proposed in this paper. Results show that our method can clarify various situations of land use of urban rail transit stations and may provide support for the application of transportation model technology. [ABSTRACT FROM AUTHOR]

In this paper, we present a framework for performing automatic analysis of Land Use Zones based on Location-Based Social Networks (LBSNs). We model city areas using a hierarchical structure of POIs extracted from foursquare. We encode such structures in kernel machines, e.g., Support Vector Machines, using a new Tree Kernel, i.e., the Hierarchical POI Kernel (HPK), which can take the importance of the individual POIs into account during the substructure matching. This way, HPK projects structures in the space of all their possible substructures such that each dimension corresponds to a semantic structural feature, weighted according to the discriminative power of POIs. We generated four different datasets for the following cities: Barcelona, Lisbon, Amsterdam and Milan, where we trained and tested our models. The results show that our approach largely outperforms previous work and standard baseline built on simple features, such as counts of different POIs. Finally, we apply a mining algorithm to extract the most relevant features (tree fragments) from the implicit TK space according to the weights the kernel machine assigned to them. Our approach can produce an explicit set of representative features that can be used to classify and characterize urban areas. [ABSTRACT FROM AUTHOR]

Fully polarimetric model-based decompositions are developed by accounting for the physical scattering model and experimental polarimetric SAR data acquisition processes. These decompositions offer the promising straightforward interpretation and highly improved inversion models for visualizing images of scattering scenarios optimally. However, the attempts in existing decompositions to implement the split real and imaginary components of the $T_{13}$ element of the coherency matrix have been hampered by the absence of physical models to fit the coherency matrix. In this paper, two additional physical scattering submodels are derived. The real and imaginary parts of $T_{13}$ are accounted for by implementing two newly developed physical scattering models. (One is for oriented dipole scattering and the other is for oriented quarter-wave reflection.) Furthermore, this paper is extended by implementing these physical models into a six-component scattering power model-based decomposition. To this date, the developed novel decompositions account for the maximum elements of the coherency matrix in a physical manner compared to the existing model-based decompositions. The proposed novel decomposition is tested on L-band and X-band fully polarimetric SAR data sets of the Advanced Land Observing Satellite-2/Phased Array L-band Synthetic Aperture Radar-2 and the X-band TerraSAR-X, respectively. This new decomposition produces additional two scattering submatrix components. Such scattering components are prevalent in vegetation and urban areas and even dominant over highly oriented urban scenarios. The new method enhances the truly existing double-bounce scattering contributions and reduces the overrated volume scattering from double-bounce scatterers. By comparing the results, it is found that the proposed decomposition considerably enhances the SAR image quality and its more correct visualizing presentation compared to existing decompositions. It is also found to be more robust over the oriented urban areas than the existing decompositions, resulting from the utilization of both the real and imaginary components of $T_{13}$ polarimetric information in a physical scattering manner. [ABSTRACT FROM AUTHOR]

In this paper, we present the results of applying global morphological texture descriptors to the problem of content-based remote sensing image retrieval. Specifically, we explore the potential of recently developed multiscale texture descriptors, namely, the circular covariance histogram and the rotation-invariant point triplets. Moreover, we introduce a couple of new descriptors, exploiting the Fourier power spectrum of the quasi-flat-zone-based scale space of their input. The descriptors are evaluated with the UC Merced Land Use-Land Cover data set, which has been only recently made public. The proposed approach is shown to outperform the best known retrieval scores, despite its shorter feature vector length, thus asserting the practical interest of global content descriptors as well as of mathematical morphology in this context. [ABSTRACT FROM AUTHOR]

With the rapid urbanization, electrical infrastructure spreads to raw areas without existing loads. How to achieve accurate long-term load forecasts based on land use plans is a realistic problem. On the other hand, load forecasting (LF) should be extended to high spatial resolutions to guide middle- or low-voltage planning and time domain to consider the impacts of distribution generations and diversified users on multi-period system demands. A data-driven bottom-up spatial and temporal LF approach is proposed in this paper to solve these challenges. Land plots are treated as basic LF resolution to describe available multi-attribute data in smart grids and modern cities. Kernel density estimation and adaptive k-means are adopted to aggregate typical load densities and profiles of different land use types. Stacked auto-encoders are utilized to forecast the unknown plot load quantities. The neighbor plot loads are summed up to obtain the estimated loads of larger areas based on clustered load profiles. Case studies demonstrate that the proposed LF is more applicable than benchmark methods both in accuracy and application potential. The estimated hierarchical spatial and temporal results are of great significance to guide load balancing, power system planning, and user integration in different voltage levels. [ABSTRACT FROM AUTHOR]

In this paper, we seek to identify the different impacts of external and internal information on taxis’ cruising behaviors and to find effective methods to enhance taxis’ cruising efficiency. Through global positioning system trajectory data collected in Shenzhen, China, we determine the impacts of external factors (land use, traffic conditions, and road grade) and internal factors (previous pick-up experience) on cruising location choice using a zero-inflated negative binomial model. The results indicate that the external factors have a more significant influence than the internal ones. Specifically, traffic conditions, land use, urban expressways, and previous pick-up points are the major factors that influence drivers’ cruising decisions. The results also show that drivers follow different patterns during different times of day, i.e., relying on traffic conditions and land use in the morning/ evening peak hours but emphasizing land use and previous pick-up experience during non-peak hours. In addition, high-earning drivers and roaming drivers prefer to cruise in areas with high-density land use and optimal traffic conditions, whereas low-earning drivers and target drivers tend to cruise in areas with more previous pick-up points. These findings uncover the underlying mechanisms of cruising decisions and facilitate the development of strategies to minimize empty cruising time. Based on the study results, the external and internal information that was found to affect cruising decisions can be released to taxi drivers to improve their cruising efficiency. This information is also helpful to managers in deciding the overall layout of taxi service locations. [ABSTRACT FROM AUTHOR]

Object-based image analysis is quickly gaining acceptance among remote sensing community, and object-based image classification methods are increasingly being used for classification of land use/cover units from high-resolution satellite images with results closer to human interpretation compared to per-pixel classifiers. The problem of nonlinear separability of classes in a feature space consisting of spectral/spatial/textural features is addressed by kernel-based nonlinear mapping of the feature vectors. This facilitates use of linear discriminant functions for classification as used in artificial neural networks (ANNs). In this paper, performance of a recently introduced kernel called cloud basis function (CBF) is investigated with some modification for classification. The CBF has demonstrated superior performance to the tune of about 4% higher classification accuracy compared to conventional radial basis function used in ANN. The results are further improved by using probabilistic relaxation labeling as a postprocessing step. This paper has potential applications in urban planning and urban studies. [ABSTRACT FROM PUBLISHER]

This paper introduces a weighted total least squares (WTLS)-based estimator into image registration to deal with the coordinates of control points (CPs) that are of unequal accuracy. The performance of the estimator is investigated by means of simulation experiments using different coordinate errors. Comparisons with ordinary least squares (LS), total LS (TLS), scaled TLS, and weighted LS estimators are made. A novel adaptive weight determination scheme is applied to experiments with remotely sensed images. These illustrate the practicability and effectiveness of the proposed registration method by collecting CPs with different-sized errors from multiple reference images with different spatial resolutions. This paper concludes that the WTLS-based iteratively reweighted TLS method achieves a more robust estimation of model parameters and higher registration accuracy if heteroscedastic errors occur in both the coordinates of reference CPs and target CPs. [ABSTRACT FROM PUBLISHER]

In this paper, we demonstrated the possibility of performing land use and land cover (LULC) classification over a wide area by an L-band polarimetric synthetic aperture radar (SAR). In previous studies, there has been scant LULC classification by polarimetric SAR data over a wide area. We used satellite-based SAR data with an area of ca. 320 000 km2 obtained by the Phased Array type L-band SAR (PALSAR)-2 phase array. We performed the LULC classification using full polarimetry (FP), compact polarimetry (CP), and dual polarimetry (DP) data by PALSAR-2 and compared their classification accuracy. Our results show FP to be the most accurate. The CP and the DP have the advantages of large-scale coverage and compact data volume but is slightly less accurate than the FP. To further improve accuracy of the classification process, texture analysis, observation date information, and feature elimination are effective. We determined the classification accuracy for seven classes to be 73.4% (the kappa coefficient is 0.668). We found the rice paddy, forest, grass, and urban areas to be sufficiently accurate (84.5%) for practical application. We compared the obtained classification map with an existing LULC map to grasp the LULC changes induced by a recent disaster and successfully detected the damage areas of the disaster. These results indicate the possibility of large-scale LULC monitoring by an L-band polarimetric SAR, which can acquire images rapidly without being affected by weather. [ABSTRACT FROM AUTHOR]

Classification of remotely sensed images into land cover or land use is highly dependent on geographical information at least at two levels. First, land cover classes are observed in a spatially smooth domain separated by sharp region boundaries. Second, land classes and observation scale are also tightly intertwined: they tend to be consistent within areas of homogeneous appearance, or regions, in the sense that all pixels within a roof should be classified as roof, independently on the spatial support used for the classification. In this paper, we follow these two observations and encode them as priors in an energy minimization framework based on conditional random fields (CRFs), where classification results obtained at pixel and region levels are probabilistically fused. The aim is to enforce the final maps to be consistent not only in their own spatial supports (pixel and region) but also across supports, i.e., by getting the predictions on the pixel lattice and on the set of regions to agree. To this end, we define an energy function with three terms: 1) a data term for the individual elements in each support (support-specific nodes); 2) spatial regularization terms in a neighborhood for each of the supports (support-specific edges); and 3) a regularization term between individual pixels and the region containing each of them (intersupports edges). We utilize these priors in a unified energy minimization problem that can be optimized by standard solvers. The proposed 2L↯CRF model consists of a CRF defined over a bipartite graph, i.e., two interconnected layers within a single graph accounting for interlattice connections. 2L↯CRF is tested on two very high-resolution data sets involving submetric satellite and subdecimeter aerial data. In all cases, 2L↯CRF improves the result obtained by the independent base model (either random forests or convolutional neural networks) and by standard CRF models enforcing smoothness in the spatial domain. [ABSTRACT FROM AUTHOR]

High spatial resolution (HSR) imagery scene classification has been the subject of increased interest in recent years, and has great potential for many applications, such as urban functional analysis. Rooted in natural information processing, the use of the probabilistic topic model (PTM) to capture latent topics to represent HSR images has been an effective way to bridge the semantic gap. However, how to effectively discover discriminative information to recognize the HSR scenes is a challenging task. In this paper, the sparse homogeneous–heterogeneous topic feature model (SHHTFM) is proposed for HSR image scene classification. Differing from the conventional PTM-based scene classification methods, which utilize only heterogeneous features, SHHTFM explores the effect of the homogeneous information. Based on the union of uniform grid sampling and simple linear iterative clustering superpixel sampling, SHHTFM exploits both the heterogeneous and homogeneous information. After separately mining different types of low-level features and latent topics, the sparse topic inference procedure of SHHTFM further improves the fusion of the sparse heterogeneous and homogeneous topics. In addition, multisource geographical data are effectively integrated, where the water and vegetation boundaries define a more accurate way to restrict the boundaries of different scenes, and are then combined with the road network data to further improve the scene annotation performance. This provides more reliable and applicable results for us to better understand the complex scenes. The experimental results obtained with two HSR image classification data sets and an HSR image annotation data set demonstrate that the proposed SHHTFM framework can solve the scene classification problem, with a high classification accuracy as well as a high time efficiency. [ABSTRACT FROM AUTHOR]

Urban landscapes present a variety of socio-topological environments that are associated to diverse human activities. As the latter affect the way individuals connect with each other, a bound exists between the urban tissue and the mobile communication demand. In this paper, we investigate the heterogeneous patterns emerging in the mobile communication activity recorded within metropolitan regions. To that end, we introduce an original technique to identify classes of mobile traffic signatures that are distinctive of different urban fabrics. Our proposed technique outperforms previous approaches when confronted to ground-truth information, and allows characterizing the mobile demand in greater detail than that attained in the literature to date. We apply our technique to extensive real-world data collected by major mobile operators in 10 cities. Results unveil the diversity of baseline communication activities across countries, but also provide evidence of the existence of a number of mobile traffic signatures that are common to all studied areas and specific to particular land uses. [ABSTRACT FROM AUTHOR]

This paper introduces a data mining method to investigate the relationship between seasonal factors (SFs) and land use characteristics for urban areas in Florida through discrete Fourier transformation (DFT). First, DFT is applied to discover seasonal variation patterns, and two typical patterns were identified. Second, linear regression is used to determine influential variables, and a weighted similarity method derived from the amplitude of each DFT wave is applied for the SF assignment. The results obtained by DFT demonstrate promising assignment accuracy with a mean absolute percentage error of 4.27% for all data and 3.96% for the low seasonal household percentage subclass. [ABSTRACT FROM PUBLISHER]

Landsat data are now available for more than 30 years, providing the longest high-resolution record of Earth monitoring. This unprecedented time series of satellite imagery allows for extensive temporal observation of terrestrial processes such as land cover and land use change. However, despite this unique opportunity, most existing change detection techniques do not fully capitalize on this long time series. In this paper, a method that exploits both the temporal and spatial domains of time series satellite data to map land cover changes is presented. The time series of each pixel in the image is modeled with a combination of: 1) pixel-specific remotely sensed data; 2) neighboring pixels derived from ground observation data; and 3) time series transition probabilities. The spatial information is modeled with variograms and integrated using indicator kriging; time series transition probabilities are combined using an information-based cascade approach. This results in a map that is significantly more accurate in identifying when, where, and what land cover changes occurred. For the six images used in this paper, the prediction accuracy of the time series improves significantly, increasing from 31% to 61%, when both space and time are considered with the maximum likelihood. The consideration of spatial continuity also reduced unwanted speckles in the classified images, removing the need for any postprocessing. These results indicate that combining space and time domains significantly improves the accuracy of temporal change detection analyses and can produce high-quality time series land cover maps. [ABSTRACT FROM AUTHOR]

Land cover and land use affect a wide range of regional-scale to global-scale ecosystem processes, and many Earth system models rely on accurate land cover information. However, multitemporal land cover products often show unrealistically high levels of year-to-year label change, particularly at coarse spatial resolution (i.e., 300–500 m). Much of this apparent land cover change arises from errors in classification and does not indicate real change in land cover or land use. In this paper, we present a novel framework that uses a hidden Markov model (HMM) to help distinguish real land cover change from spurious land cover changes in classification time series. We apply the HMM as a postprocessing step to supervised classification, and we solve for the optimal label sequence using existing HMM algorithms. Our results demonstrate that the HMM provides a rigorous framework for capturing temporal context and likelihood of land cover change at each pixel. We evaluated our approach using the MODIS Collection 5.1 Land Cover Type product (MCD12Q1), focusing on areas that have experienced little change over the MODIS time series, and areas that have experienced well characterized change (e.g., deforestation). We show that the HMM method provides label sequences that are more accurate and that exhibit less year-to-year variability than label sequences produced by ensemble-decision-tree classification or by postprocessing heuristics that have been used in recent versions of MCD12Q1 product. The framework that we present offers an improvement over conventional multitemporal land cover classification methods, and it is widely applicable to problems in multitemporal land cover and land use monitoring. [ABSTRACT FROM PUBLISHER]

Due to the rapid technological development of various different satellite sensors, a huge volume of high-resolution image data sets can now be acquired. How to efficiently represent and recognize the scenes from such high-resolution image data has become a critical task. In this paper, we propose an unsupervised feature learning framework for scene classification. By using the saliency detection algorithm, we extract a representative set of patches from the salient regions in the image data set. These unlabeled data patches are exploited by an unsupervised feature learning method to learn a set of feature extractors which are robust and efficient and do not need elaborately designed descriptors such as the scale-invariant-feature-transform-based algorithm. We show that the statistics generated from the learned feature extractors can characterize a complex scene very well and can produce excellent classification accuracy. In order to reduce overfitting in the feature learning step, we further employ a recently developed regularization method called “dropout,” which has proved to be very effective in image classification. In the experiments, the proposed method was applied to two challenging high-resolution data sets: the UC Merced data set containing 21 different aerial scene categories with a submeter resolution and the Sydney data set containing seven land-use categories with a 60-cm spatial resolution. The proposed method obtained results that were equal to or even better than the previous best results with the UC Merced data set, and it also obtained the highest accuracy with the Sydney data set, demonstrating that the proposed unsupervised-feature-learning-based scene classification method provides more accurate classification results than the other latent-Dirichlet-allocation-based methods and the sparse coding method. [ABSTRACT FROM PUBLISHER]

With the increased spatial resolution of current sensor constellations, more details are captured about our changing planet, enabling the recognition of a greater range of land use/land cover classes. While pixeland object-based classification approaches are widely used for extracting information from imagery, recent studies have shown the importance of spatial contexts for discriminating more specific and challenging classes. This paper proposes a new compact representation for the fast query/classification of compound structures from very high resolution optical remote sensing imagery. This bag-of-features representation relies on the multiscale segmentation of the input image and the quantization of image structures pooled into visual word distributions for the characterization of compound structures. A compressed form of the visual word distributions is described, allowing adaptive and fast queries/classification of image patterns. The proposed representation and the query methodology are evaluated for the classification of the UC Merced 21-class data set, for the detection of informal settlements and for the discrimination of challenging agricultural classes. The results show that the proposed representation competes with state-of-the-art techniques. In addition, the complexity analysis demonstrates that the representation requires about 5% of the image storage space while allowing us to perform queries at a speed down to 1 s/ 1000 km2/CPU for 2-m multispectral data. [ABSTRACT FROM PUBLISHER]

The rich data provided by high-resolution satellite imagery allow us to directly model aerial scenes by understanding their spatial and structural patterns. While pixel- and object-based classification approaches are widely used for satellite image analysis, often these approaches exploit the high-fidelity image data in a limited way. In this paper, we explore an unsupervised feature learning approach for scene classification. Dense low-level feature descriptors are extracted to characterize the local spatial patterns. These unlabeled feature measurements are exploited in a novel way to learn a set of basis functions. The low-level feature descriptors are encoded in terms of the basis functions to generate new sparse representation for the feature descriptors. We show that the statistics generated from the sparse features characterize the scene well producing excellent classification accuracy. We apply our technique to several challenging aerial scene data sets: ORNL-I data set consisting of 1-m spatial resolution satellite imagery with diverse sensor and scene characteristics representing five land-use categories, UCMERCED data set representing twenty one different aerial scene categories with sub-meter resolution, and ORNL-II data set for large-facility scene detection. Our results are highly promising and, on the UCMERCED data set we outperform the previous best results. We demonstrate that the proposed aerial scene classification method can be highly effective in developing a detection system that can be used to automatically scan large-scale high-resolution satellite imagery for detecting large facilities such as a shopping mall. [ABSTRACT FROM AUTHOR]

The land surface reflectance data are indispensable to generate many other land products. Global land surface reflectance data have been routinely produced from remote sensing sensors aboard different satellite platforms. However, the original data, especially the daily data, suffer from a large number of spatial gaps, which result from atmospheric contamination and instrument deficiencies. This seriously limits their further applications. Many composite products with less spatial gaps have been generated to solve the above problem, but they easily sacrifice their temporal resolutions of original data. Even worse, they cannot be directly implemented in realistic applications because of the noise and composite seams. This paper proposes a temporal–spatial reconstruction method (TSRM) to generate daily gapless and seamless land surface reflectance data. The TSRM integrates both temporal and spatial information for recovering different land cover types using three processing steps. First, spatial gaps are coarsely filled with multiyear weighted average (Step1). After that, all the gaps that are not filled in the first step are interpolated by using harmonic analysis of time series with true value constraint (Step2). Finally, the reconstructed results in the last step are seamlessly processed using the Poisson image editing method, and the seamless daily reflectance data set is generated (Step3). The Moderate Resolution Imaging Spectroradiometer reflectance data set (MOD09GA and MYD09GA) on two testing areas is selected to verify the performance of the proposed TSRM. Experimental results show that the TSRM has good performance with regard to maintaining the temporal and spatial integrity of the daily land surface reflectance data. Results on different testing sites also demonstrate that the TSRM preserves spectral integrity with clear seasonal trends for each spectral band. [ABSTRACT FROM AUTHOR]

The use rate of urban land is a significant sign to evaluate urban construction, and scene recognition has important application value in improving urban land use rate. In this paper, a new lightweight model based on VGG16 is proposed to extract distinct features of remote sensing images through five convolution modules. This model uses depthwise separable convolution to reduce the network parameters. An adaptive pooling layer is added to solve the inherent non-adaptive problem of the convolution network. It makes the network insensitive to the size of the input image. The global average pooling layer is used to sum the information to make the input spatial transformation more stable. This paper conducts training and testing on two data sets, NWPU-RESISC45 Dataset and SIRI-WHU Dataset, and the recognition scenarios are 13 and 12 categories. Experimental results show that this method is better than other models in recognition accuracy and model size.

This paper presents a new unsupervised land cover/land use classification scheme using polarimetric synthetic aperture radar (PolSAR) imagery based on polarimetric scattering similarity. Compared with the H/alpha classification scheme based on a dominant “average” scattering mechanism, the proposed scheme has such advantages as the following: 1) The major scattering mechanism represents a target scattering in the low-entropy case; 2) it also represents both the major and minor scattering mechanisms in the medium-entropy case; and 3) all the scattering mechanisms in the high-entropy case can be represented. The major and minor scattering mechanisms have been identified automatically based on the relative magnitude of multiple-scattering similarities. The canonical scattering corresponding to maximum scattering similarity is regarded as the major scattering mechanism. The result obtained using the National Aeronautics and Space Administration/Jet Propulsion Laboratory's AIRSAR L-band PolSAR imagery reveals that the proposed scheme is more effective as compared to the existing models and promises to increase the accuracy of the classification and interpretation. [ABSTRACT FROM PUBLISHER]

A multi-agent system for spatial electric load forecasting, especially suited to simulating the different social dynamics involved in distribution systems, is presented. This approach improves the spatial forecasting techniques that usually consider the service zone as a static entity to model or simulate the spatial electric load forecasting in a city. This paper aims to determine how the electric load will be distributed among the sub-zones in the city. For this, the service zone is divided into several subzones, each subzone considered as an independent agent identified with a corresponding load level, and their relationships with the neighbor zones are represented through development probabilities. These probabilities are considered as input data for the simulation. Given this setting, different kinds of agents can be developed to simulate the growth pattern of the loads in distribution systems in parallel. The approach is tested with data from a real distribution system in a mid-size city; the results show a low spatial error when compared to real data. Less than 6% of the load growth was identified 0.71 km outside of its correct location on the test system. [ABSTRACT FROM PUBLISHER]

Land surface radiation and energy budgets are critical to address a variety of scientific and application issues related to climate trends, weather predictions, hydrologic and biogeophysical modeling, and the monitoring of ecosystem health and agricultural crops. This is an introductory paper to the special issue of land surface radiation and energy budgets from an international conference on this subject. The temporal trends of these components are first discussed in the context of climate change and human induced land use changes. After a brief introduction to the conference, results from each paper of this special issue are summarized. [ABSTRACT FROM PUBLISHER]

In September 2000, an airborne synthetic aperture radar (SAR) mission acquired unprecedented full polarimetric P-band data over the Tapajós National Forest (Pará State), which is an area in the Brazilian Amazon which has been continuously monitored in the last three decades. Eight land use/cover classes were identified, namely, primary forest, regeneration older than 25 years, regeneration between 12 and 25 years, regeneration between 6 and 12 years, regeneration younger than six years, crops/pasture, bare soil, and floodplain (FP). The objective of this paper is to analyze the potential of full polarimetric P-band data in distinguishing different land use/cover classes with a minimum established Kappa value of 75%, using the latest development on SAR statistical characterization. The iterated conditional mode (ICM) contextual classifier was applied to amplitude, intensity images, biomass index, and some polarimetric parameters (entropy, α angle, and anisotropy) extracted from the polarimetric P-band data. As the accuracy obtained for eight classes was not acceptable, another two sets, with five and four classes, were formed by the combination of the previous ones. They were defined by confusion matrix analysis and by the graphical analysis of average backscatter values, entropy, α angle, and anisotropy images and by the H/α plans of the land use samples. The classification accuracy with four classes (three levels of biomass plus FP) was then considered acceptable with a Kappa value of 76.81%, using the ICM classification with the adequate bivariate distribution for the HV and VV channels. [ABSTRACT FROM AUTHOR]

With the development of society and economy, the urban rail transit has become one of the important components of urban transportation system, while the construction of the urban rail greatly improves the public transportation environments. Currently, there are many research focus on the passenger flow predictions according to their corresponding historical data, however, it is hard to assist transport models vary such volumes for a new station planning or being constructed. In view of this limitation, we provide a novel method for urban rail station characteristics analysis in intelligent transportation considering city land usages. Initially, point of interest (POIs) are divided by the proposed RC-tree (Colored R-tree)-based algorithm into the bounded areas for each station. Second, the Diversity and Proportion approaches are proposed to extract the $top$ - $k$ POIs from bounded areas based on their semantic and spatial characteristics. Then, classify the stations based on the similarity of the extracted $top$ - $k$ POIs. Moreover, we made a case study on real dataset, including a large volume of Automatic Fare Collection system (AFC) records for the experimental evaluations, and the results show that the proposed method can verify the rationality of land use and provide support for the application of transportation model technology. [ABSTRACT FROM AUTHOR]

Updating the existing land-use maps with remote sensing imagery has become a common method to produce the latest land-use database. An important step is to extract the changed information. In this paper, we propose a novel method to extract the changed parcels in land-use maps by using the holistic feature called “Spatial Envelope,” which encodes each parcel without segmenting it into homogeneous objects or small regions. The holistic feature is based on the energy spectrum of the windowed Fourier transform (WFT) of each land-use parcel, which is ideal for scene categorization. Unlike the pixel-based change detection using the difference image (DI) leading to speckled results or object-based method which requires a complicated process to segment the land-use parcel into homogeneous land-cover objects, our parcel-based change detection treats each land-use parcel as an entirety by calculating the holistic feature for the former and latter parcels. Then, the distance between the corresponding former and latter parcels is compared against a threshold to select the changed parcels. Experiments have demonstrated that our procedure can extract the changed parcels with the overall accuracy of more than 92%. The performance of our procedure is reliable not only on the high-resolution (HR) images of the same sensor, but also on the images acquired by different sensors with the same or approximate spatial resolution. Comparative experiments have also proved that the holistic feature is better than conventional spectral and textural features in parcel-based change detection. [ABSTRACT FROM PUBLISHER]

Grasslands cover approximately 40% of the Earth's surface. Low-cost tools for inventory, management, and monitoring are needed because of their great expanse, diversity, and the importance for environmental processes. Remote sensing is a useful technique for providing accurate and reliable information for land use planning and large-scale grassland management. In the context of “GIO land” (Copernicus Initial Operations land program), which is currently contracted by the European Environment Agency, a high-resolution grassland layer of 39 European countries is being created with an overall classification accuracy of better than 80%. Since grassland canopy density, chlorophyll status, and ground cover (GC) are highly dynamic throughout the growing season, no unique spectral signature can be used to map grasslands. Therefore, it is necessary to use image time series to characterize the phenological dynamics of grasslands throughout the year in order to discriminate between grasslands and other vegetation with similar spectral responses. This paper describes an operational approach based on a multisensor concept that employs optical multitemporal and multiscale satellite imagery to generate the high-resolution pan-European grassland layer. The approach is based on the supervised decision tree classifier C5.0 in combination with previous image segmentation and seasonal statistics for various vegetation indices (VIs). Results from the grassland classification for Hungary are presented. The accuracy assessment for this classification was carried out using 328 independent sample points derived from a ground-based European field survey program (LUCAS) and current CORINE Land Cover data. The grassland classification approach is explained in detail on the example of Hungary where an overall accuracy of 92.2% has been reached. [ABSTRACT FROM PUBLISHER]

One challenge to implementing spectral change detection algorithms using multitemporal Landsat data is that key dates and periods are often missing from the record due to weather disturbances and lapses in continuous coverage. This paper presents a method that utilizes residuals from harmonic regression over years of Landsat data, in conjunction with statistical quality control charts, to signal subtle disturbances in vegetative cover. These charts are able to detect changes from both deforestation and subtler forest degradation and thinning. First, harmonic regression residuals are computed after fitting models to interannual training data. These residual time series are then subjected to Shewhart X-bar control charts and exponentially weighted moving average charts. The Shewhart X-bar charts are also utilized in the algorithm to generate a data-driven cloud filter, effectively removing clouds and cloud shadows on a location-specific basis. Disturbed pixels are indicated when the charts signal a deviation from data-driven control limits. The methods are applied to a collection of loblolly pine ( Pinus taeda) stands in Alabama, USA. The results are compared with stands for which known thinning has occurred at known times. The method yielded an overall accuracy of 85%, with the particular result that it provided afforestation/deforestation maps on a per-image basis, producing new maps with each successive incorporated image. These maps matched very well with observed changes in aerial photography over the test period. Accordingly, the method is highly recommended for on-the-fly change detection, for changes in both land use and land management within a given land use. [ABSTRACT FROM PUBLISHER]

Polarimetric synthetic aperture radar (PolSAR) data are used extensively for terrain classification applying SAR features from various target decompositions and certain textural features. However, one source of information has so far been neglected from PolSAR classification: Color. It is a common practice to visualize PolSAR data by color coding methods and thus, it is possible to extract powerful color features from such pseudocolor images so as to provide additional data for a superior terrain classification. In this paper, we first review previous attempts for PolSAR classifications using various feature combinations and then we introduce and perform in-depth investigation of the application of color features over the Pauli color-coded images besides SAR and texture features. We run an extensive set of comparative evaluations using 24 different feature set combinations over three images of the Flevoland- and the San Francisco Bay region from the RADARSAT-2 and the AIRSAR systems operating in C- and L-bands, respectively. We then consider support vector machines and random forests classifier topologies to test and evaluate the role of color features over the classification performance. The classification results show that the additional color features introduce a new level of discrimination and provide noteworthy improvement in classification performance (compared with the traditionally employed PolSAR and texture features) within the application of land use and land cover classification. [ABSTRACT FROM PUBLISHER]

With the advancement of machine learning, classification methods have been increasingly used in change (or transition) detection. The texton forest (TF)-based method has received increasing research attention because of its speed, good generalization characteristics, stability, and especially its ability to capture spatial contextual information. In this paper, we propose a TF-based method for transition detection in remotely sensed imagery. We investigate a maximal joint-information gain criterion for random forests to better capture combined information in the bi-temporal images in transition detection, which is implemented by a natural extension of binary-trees in traditional methods into a quad-decision tree structure. We also utilize color-invariant gradient as a feature to help alleviate the impact of difference in imaging conditions on bi-temporal transition detection. The experimental results for transition detection show that our bi-temporal TF classifier achieves better performance than a post-classification comparison method and several other alternative methods. [ABSTRACT FROM AUTHOR]

This paper proposes an integration of Spectral Mixture Analysis and Endmember Remote Sensing Indices to derive land surface temperature (LST), to identify urban heat islands (UHI) and to investigate their relationships to land use/land cover (LULC) and air-pollution in Tabriz city, Iran. LST values are assessed from ASTER remotely sensed thermal images to appraise the intensity of UHIs through a statistical approach. The results indicate that LST is highly influenced by LULC and that UHIs are closely linked to LST and LULC. As expected, LST is sensitive to vegetation and moisture and low temperatures are found in water bodies and vegetated areas. High temperatures are related to construction zones and industrial sites which are not necessarily located in the city centre. While the results demonstrate a high correlation between UHIs and air-pollution spatial analysis discloses a different pattern: when correlating identified UHI zones with air pollution the market area of Tabriz city reveals the highest measured PM values. [ABSTRACT FROM PUBLISHER]

An individual tree within a forest stand will have its height and diameter growth restricted by the influence of neighboring trees. This is because trees in close proximity compete for resources and space to enable growth. In this paper, the position of trees, tree height (LH), tree crown radius (LCR), and growth competition index (LCI) were extracted from a light-detection-and-ranging (LiDAR)-based rasterized canopy height model using the multilevel morphological active-contour algorithm. The diameter and volume of individual trees are tested and validated to be an exponential function of those LiDAR-derived tree parameters. The best LiDAR-based diameter estimation model and volume estimation model were tested as significant with an R^2 value of 0.84 and 0.9 and evaluated with an estimation bias of 8.7 cm and 0.91 \m^3, respectively. Results also showed that LH and LCR are positively related to the LiDAR-derived diameter at breast height (DBH) and the LiDAR-derived volume of individual trees in a forest stand, whereas LCI is negatively related. The proposed algorithm of individual tree volume estimation was further applied to predict the volume of three sample plots in mountainous forest stands. It was found that the LVM could be used to predict an acceptable volume estimate of old-aged forest stands. The estimation bias, i.e., percentage RMSE (RMSE%), is averaged at around 4% using the LiDAR metrics \ln\LH, LCI, and LCR, whereas the RMSE% increases to 50% if only \ln\LH is applied. Results suggest that LCI is an important regulation factor in the estimation of forest volume stocks using LiDAR remote sensing. [ABSTRACT FROM PUBLISHER]

The Moderate Resolution Imaging Spectroradiometer (MODIS) has been used in several remote sensing studies, including land, ocean, and atmospheric applications. The advantages of this sensor are its high spectral resolution, with 36 spectral bands; its high revisiting frequency; and its public domain availability. The first seven bands of MODIS are in the visible, near-infrared, and mid-infrared spectral regions of the electromagnetic spectrum which are sensitive to spectral changes due to deforestation, burned areas, and vegetation regrowth, among other land-use changes, making near-real-time forest monitoring a suitable application. However, the different spatial resolution of the spectral bands placed in these spectral regions imposes challenges to combine them in forest monitoring applications. In this paper, we present an algorithm based on geostatistics to downscale five 500-m MODIS pixel bands to match two 250-m pixel bands. We also discuss the advantages and limitations of this method in relation to existing downscaling algorithms. Our proposed method merges the data to the best spatial resolution and better retains the spectral information of the original data. [ABSTRACT FROM PUBLISHER]

The spatial resolution of thermal infrared (TIR) instruments is often not sufficient for many applications, but this low resolution is counterbalanced by the high temporal resolution (for example the SEVIRI instrument onboard the European Meteosat 8 and 9 presents a spatial resolution of 3 km \times 3 km at nadir and a temporal resolution of 15 mn). At kilometric scales, the observed pixel is generally heterogeneous in terms of land cover, and the temperatures of the different components may present large discrepancies. This paper presents a methodology to infer the temperatures of the various land cover/use classes composing a mixed pixel, from a whole pixel measurement. To infer intra-pixel temperature, information on the mixture within each low resolution pixel, e.g., the proportions of the land cover types derived from high spatial resolution imaging, account for a first constraint. However, in the absence of supplementary constraints, the number of unknown variables is greater than the number of measurements, and there is not uniqueness of the solution. Thus, we propose to take advantage of a priori knowledge provided by a land surface model (LSM), and of the temporal and spatial correlation features of the surface temperature. We propose a new downscaling method for estimating sub pixel signal. It applies to TIR data and: the inversion procedure provides as a result, the land surface temperature (LST) temporal series of each land cover/use class (called endmember) constituting the coarse resolution pixel. Three kinds of a priori information have been introduced, namely 1) a first guess subpixel temperature derived from the SEtHyS LSM; 2) a Markov Random Chain model of the surface temperature temporal dependencies from times t to t + 1 ; 3) a Markov Random Field model of the spatial dependencies between endmember temperatures. Then, the “Maximum A Posteriori” estimator provides the most likely endmember temperatures, given 1) the observed coarse resolution temperatures, 2) the composition of the pixels in terms of “land cover/land use,” and 3) the LSM first guess subpixel temperature values, 4) the a priori spatial and temporal Markov models. The performance of this new method has been first evaluated on simulated data (random Gaussian variables with means equal to endmember temperatures simulated using LSM). The method accuracy versus the observation errors and the number of endmembers was analyzed. The algorithm was then run on actual data, namely Meteosat SEVIRI Land Surface products acquired over an agricultural region in southeastern France. The performance evaluation was done by comparing the subpixel LST estimations to the high-resolution temperatures provided by the Terra/ASTER instrument. Due to the huge bias between sensors ( \sim4 K), an intercalibration preprocessing between SEVIRI and ASTER was done. In this case, the achieved RMSE is lower than 2 K. [ABSTRACT FROM PUBLISHER]

This paper investigates local invariant features for geographic (overhead) image retrieval. Local features are particularly well suited for the newer generations of aerial and satellite imagery whose increased spatial resolution, often just tens of centimeters per pixel, allows a greater range of objects and spatial patterns to be recognized than ever before. Local invariant features have been successfully applied to a broad range of computer vision problems and, as such, are receiving increased attention from the remote sensing community particularly for challenging tasks such as detection and classification. We perform an extensive evaluation of local invariant features for image retrieval of land-use/land-cover (LULC) classes in high-resolution aerial imagery. We report on the effects of a number of design parameters on a bag-of-visual-words (BOVW) representation including saliency- versus grid-based local feature extraction, the size of the visual codebook, the clustering algorithm used to create the codebook, and the dissimilarity measure used to compare the BOVW representations. We also perform comparisons with standard features such as color and texture. The performance is quantitatively evaluated using a first-of-its-kind LULC ground truth data set which will be made publicly available to other researchers. In addition to reporting on the effects of the core design parameters, we also describe interesting findings such as the performance-efficiency tradeoffs that are possible through the appropriate pairings of different-sized codebooks and dissimilarity measures. While the focus is on image retrieval, we expect our insights to be informative for other applications such as detection and classification. [ABSTRACT FROM PUBLISHER]

This paper presents a fuzzy expert system for the creation of landslide possibility maps using change of land-use data from Earth observation, as well as historical, rainfall, and earthquake data stored in a geographic information system, as input. The difference with other systems is in the use of change (differential) input data. The method is tested with 16 documented landslides. The fuzzy neural network (NN) developed can predict the crowns of 13 out of the 16 landslides to be among the 5% most at-risk pixels that are identified in the area of study, which covers 100 km². The fuzzy expert system considers the rules that increase the possibility of a landslide, as supplied by experts, and expresses them in the form of an empirical algebraic formula. It then fuzzifies the various thresholds they rely on and, in conjunction with uncertainties that are reported by the classifier that decides the land-use change, produces a fuzzy algebraic formula that may be used to identify the range of uncertainty in the possibility of a landslide in terms of the ranges of uncertainty in the input variables. This formula is used to train an Ishibuchi fuzzy NN, which has been designed to capture uncertainty in the rules and uncertainty in the input variables. It is this Ishibuchi NN that acts as a fuzzy expert system. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive comparison of various Machine Learning (ML) classifiers for urban land use identification using social networks data. Two analysis cycles were conducted, with the second cycle introducing the “popularity index” parameter. The results demonstrate that incorporating the popularity index significantly improved the accuracy rates of all classifiers. The Convolutional Neural Network (CNN) consistently outperformed other classifiers with a 0.9 accuracy, but the key highlight was the popularity index parameter, which optimized urban land use identification. By providing crucial contextual information and normalizing raw tweet counts, the popularity index proved vital for leveraging social media data in urban land use classification. Our findings support the value of social media data for analyzing urban land use dynamics and highlight ML classifiers with the popularity index as a promising approach to monitoring and understanding ever-changing patterns. This research makes a significant contribution to data-driven urban planning by highlighting the capacity of social media data to provide real-time and detailed insights into urban activities and land use patterns. These insights, in turn, can inform and enhance strategies for sustainable urban development and resource allocation, making them more informed and effective.