In this paper, we propose a novel forecast method which addresses the difficulty in short-term solar irradiance forecasting that arises due to rapidly evolving environmental factors over short time periods. This involves the forecasting of Global Horizontal Irradiance (GHI) that combines prediction sky images with a Radiative Transfer Model (RTM). The prediction images (up to 10 min ahead) are produced by a non-local optical flow method, which is used to calculate the cloud motion for each pixel, with consecutive sky images at 1 min intervals. The Direct Normal Irradiance (DNI) and the diffuse radiation intensity field under clear sky and overcast conditions obtained from the RTM are then mapped to the sky images. Through combining the cloud locations on the prediction image with the corresponding instance of image-based DNI and diffuse radiation intensity fields, the GHI can be quantitatively forecasted for time horizons of 1–10 min ahead. The solar forecasts are evaluated in terms of root mean square error (RMSE) and mean absolute error (MAE) in relation to in-situ measurements and compared to the performance of the persistence model. The results of our experiment show that GHI forecasts using the proposed method perform better than the persistence model.

Contradictions in spatial resolution and temporal coverage emerge from earth observation remote sensing images due to limitations in technology and cost. Therefore, how to combine remote sensing images with low spatial yet high temporal resolution as well as those with high spatial yet low temporal resolution to construct images with both high spatial resolution and high temporal coverage has become an important problem called spatio-temporal fusion problem in both research and practice. A Multi-Dictionary Bayesian Spatio-Temporal Reflectance Fusion Model (MDBFM) has been proposed in this paper. First, multiple dictionaries from regions of different classes are trained. Second, a Bayesian framework is constructed to solve the dictionary selection problem. A pixel-dictionary likehood function and a dictionary-dictionary prior function are constructed under the Bayesian framework. Third, remote sensing images before and after the middle moment are combined to predict images at the middle moment. Diverse shapes and textures information is learned from different landscapes in multi-dictionary learning to help dictionaries capture the distinctions between regions. The Bayesian framework makes full use of the priori information while the input image is classified. The experiments with one simulated dataset and two satellite datasets validate that the MDBFM is highly effective in both subjective and objective evaluation indexes. The results of MDBFM show more precise details and have a higher similarity with real images when dealing with both type changes and phenology changes.

Estimates of gross domestic product (GDP) play a significant role in evaluating the economic performance of a country or region. Understanding the spatiotemporal process of GDP growth is important for estimating or monitoring the economic state of a region. Various GDP studies have been reported, and several studies have focused on spatiotemporal GDP variations. This study presents a map spectrum-based clustering approach to analyze the spatiotemporal variation patterns of GDP growth. First, a sequence of nighttime light images (from the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS)) is used to support the spatial distribution of statistical GDP data. Subsequently, the time spectrum of each spatial unit is generated using a time series of dasymetric GDP maps, and then the spatial units with similar time spectra are clustered into one class. Each category has a similar spatiotemporal GDP variation pattern. Finally, the proposed approach is applied to analyze the spatiotemporal patterns of GDP growth in the Wuhan urban agglomeration. The experimental results illustrated regional discrepancies of GDP growth existed in the study area.

Semantic segmentation of airborne laser scanning (ALS) point clouds is a valuable yet challenging task in remote sensing. When processing large-scale ALS scenes, it is necessary to partition them into smaller blocks for ease of handling. However, this partitioning introduces a challenge in capturing the ample spatial context within each block to adequately recognize the objects with a significant spatial span. This limitation becomes particularly pronounced when relying solely on the 3-D representations as the input of neural networks. To incorporate sufficient contextual information in ALS data semantic segmentation, we propose a multimodal-based segmentation framework called projection-based context embedding (PCE) in this study. PCE effectively combines the advantages of 2-D image and 3-D point-voxel representations, which are the computational efficiency and the representation capability for fine-grained 3-D geometries. The 2-D projection is used to encode a large-scale semantic context, which is computationally expensive to be obtained using only pure 3-D representation. Simultaneously, the sparse-point-voxel convolution (SPVConv) is employed to focus on learning 3-D features from a small block of points centered on the large-scale context. Finally, to fully exploit the power of each modality, the embedding disentangling (ED) strategy is proposed additionally to combine the context embedding from the 2-D image with 3-D features for the final prediction. We demonstrate the state-of-the-art performance of PCE through extensive experiments on public large-scale ALS point cloud datasets.

In this paper, a unified framework based on rank minimization (UFRM) is proposed for use with multiangle multi/hyperspectral remote sensing images, which simultaneously integrates image superresolution reconstruction and image registration. With the complementary information of different angle images and the high correlation between each band of the multi/hyperspectral images, a new image observation model is established to describe the mathematical degradation process of the observed low-resolution multiangle multi/hyperspectral images from the desired high-resolution multi/hyperspectral image. Based on the rank-one structure of the multiangle images, each observed image is decomposed into a foreground image for each angle image, and a background image, which is shared among all the multiangle images. A multichannel total variation (MTV) constraint is applied to the target high-resolution background image, with the consideration of the high correlation of different bands. Finally, an alternating minimization optimization strategy is utilized to resolve the joint cost function, which consists of the unknown image registration transformation parameters and the desired reconstruction image. As a result, the UFRM method can simultaneously achieve image registration and superresolution reconstruction. A number of experiments were conducted, which confirmed the superior performance of the proposed method.

A greenway is a low-speed road system built on high-level afforestation, and this system serves as a venue for sightseeing, relaxation, and exercise. In China, greenway planning and construction have been actively and successfully implemented to strengthen the construction of urban human settlements. However, a specific method has yet to be used to assess the service capability of urban greenways. On the basis of geographic information systems and geographical spatial data, we propose an approach to evaluate the overall service capability of urban greenways based on three different aspects. In the first aspect, a buffer-based service-level analysis method is applied to statistically analyze population and residential quarter areas within a greenway service coverage area. This aspect can also indicate the service level and scope of urban greenways. In the second aspect, minimum distance-based accessibility analysis method is utilized for the graduation statistics of residential quarter areas in different ranges of reach distance. This aspect can further reveal the service convenience of urban greenways. In the third aspect, a calculation model is built on the basis of regular grids, to analyze the value of comprehensive service capability in each grid, and to produce a spatial distribution map of the comprehensive service capability of urban greenways. This aspect can describe the service quality of urban greenways. The analysis results of these aspects can be integrated to identify service conditions in which an urban greenway is available to urban populations and residential zones, and to obtain the comprehensive service capability value of greenways. This approach can also emphasize the limitations of greenway construction, and thus help urban planners and decision-makers create optimized urban greenway designs.