Flood segmentation using synthetic aperture radar (SAR) images is essential for determining the extent of inundation areas, and informing subsequent management recommendations. However, existing networks for flood segmentation using single modality SAR images often face inherent challenges, including interference from terrain shadows and water-like surfaces, leading to degraded segmentation performance. In this study, we introduced a multi-level multi-modal fusion network (FloodNet), in which an Adaptive Gated Feature Fusion Module (AGFFM) is designed to integrate multi-modal features from Sentinel-1 SAR images, Digital Elevation Model (DEM) and Joint Research Centre Global Surface Water (JRC-gsw). Furthermore, we proposed a semantic consistency constraint strategy to alleviate the blurring of water edges during the prediction process. Experiments on two publicly available flood datasets, C2S-Flood and ETCI-Flood, demonstrate the competitive performance of the proposed FloodNet compared with other state-of-the-art single- and multi-modal networks. The code is available at https://github.com/SuperPixelPioneer/Flood-Net.

Nighttime light (NTL) data holds irreplaceable value in research related to human activities, sustainable development, etc. However, the temporal and spatial continuity of high-resolution NTL data is challenging to meet the demands of large-scale applications. Spatiotemporal data fusion methods, by integrating low-resolution and high-resolution data, can fill in missing high-resolution data. Nevertheless, previous spatiotemporal data fusion research has primarily focused on multispectral data and face challenges when directly applied to NTL data. This research proposes a spatiotemporal fusion method specifically for NTL data, named the "LIght ON spatiotemporal data fusion" (LION) model. Experimental results indicate that LION demonstrated potential in predicting abrupt changes in NTL.

The Universal Thermal Climate Index (UTCI) is a crucial temperature index for describing human thermal comfort. With the continuous advancement of earth observation technologies, it has become feasible to monitor hourly global UTCI at kilometer-level resolution. However, the computational efficiency of UTCI calculations limits the production and application of UTCI, particularly time-series UTCI application at fine resolution. To address the abovementioned issue, this letter proposes a CUDA UTCI (CUTCI) method based on the graphics processing unit (GPU). CUTCI leverages the parallel computing capabilities of GPUs and kernel fusion techniques to improve parallelization and mitigate overhead during calculation. Experimental results demonstrated that, in comparison to operational UTCI and Thermofeel UTCI, CUTCI significantly improved computational efficiency by over 250 times and 17 times, respectively. Production of a single-period global UTCI at 0.1° resolution consumed less than 0.2 seconds. Experimental results revealed that CUTCI holds practical value in supporting real-time UTCI analysis and historical big data analysis over long time series.

Spatiotemporal data fusion provides an efficacious strategy for addressing data gaps within time series datasets. This approach significantly enhances the feasibility of large-scale remote sensing applications by, for example, enabling the creation of seamless Data Cubes (SDC). Nevertheless, strict data input requirements and low computational efficiency of current methods severely limit the practicality of large-scale SDC production. In this study, we propose an efficient spatiotemporal data fusion method, the Fast Variation-based Spatiotemporal Data Fusion (FastVSDF) method. FastVSDF consists of 3 steps, i.e., unmixing, distributing global residuals, and distributing local residuals. In the unmixing process, FastVSDF introduces the fast abundant variation classification (FAVC) to mitigate sample imbalance and expedite the unsupervised classification. Then, the in-class Gaussian weight function is introduced to accelerate the distribution of local residuals by considering the classification to introduce the information on spectral similarity. Besides, FastVSDF employs Fast Guided Filter to combat the "block artifacts" of global residuals efficiently. Results show that FastVSDF demonstrated superior performance over Fit-FC, STARFM, RASDF, and FSDAF. More importantly, FastVSDF yields a remarkable improvement in computational efficiency, reducing predicting time by 43 to 573 times. As a practical application, we generated the Sentinel-2 SDC for the Yangtze River Basin, China. The fusion process for a single period’s Yangtze River Basin dataset was accomplished within 20 minutes, with an average of 3.85 seconds for each Sentinel-2 scene. Comprehensively considering the efficiency, accuracy, feasibility, and universality, FastVSDF demonstrates the practical potential for constructing large-scale and long-term SDC. Our code will be publicly available at https://github.com/ChenXuAxel/FastVSDF.

Tiled remote sensing data is essential for web-based remote sensing applications, widely applied in online map services, cloud-based remote sensing processing, etc. However, most existing tiling algorithms focus on tiling single remote sensing images with stand-alone machines. As the volume of remote sensing data increases, the demand for tiling high-resolution and large-scale remote sensing datasets increases dramatically. In this research, we propose a cloud-based parallel tiling algorithm for large-scale remote sensing datasets. A three-step processing flow is designed to implement the tiling of datasets composed of a set of images. Furthermore, three types of cloud-based storage are adopted to improve the efficiency of data extraction, namely, cloud storage, block storage, and NoSQL. We experimented with the proposed algorithm for tiling a national-scale 2 m resolution remote sensing dataset. The whole process took about 25.7 hours with up to 180 cores.

Today, remote sensing (RS) data are already regarded as “big data.” Developments in computer science have made it possible to explore the potential treasure within remote sensing big data, but only limited remote sensing research has made use of big data technology due to gaps in techniques between big data and remote sensing. In this research, we analyzed the full processing flow of remote sensing big data from the perspective of both computer science and remote sensing science and proposed a modular framework. Computation ready data (CRD), a dynamic data type for computation based on analysis ready data (ARD), is proposed to connect the two main modules of the framework, the data module and computation module. Compared with existing research, the proposed framework classifies and abstracts the key technical and research points of the processing of remote sensing big data as replaceable modules and bridges them through an open organization. Subsequently, we built a prototype platform with open-source technologies and carried out three experiments to validate the feasibility and advantages of the framework, namely normalized difference vegetation index (NDVI) production, water body change detection, and land use classification. Results indicate that this framework can greatly reduce experimental costs for remote sensing researchers. While the proposed framework has proven flexible and practical, further research is needed for the technical implementation of certain modules to achieve the original intention of the framework.

Vehicle platooning for highway driving has many benefits, such as lowering fuel consumption, improving traffic safety, and reducing traffic congestion. However, its performance could be undermined due to uncertainty. This work proposes a new control method that combines distributed stochastic model predictive control with Taguchi’s robustness (TR-DSMPC) for vehicle platooning. The proposed method inherits the advantages of both Taguchi’s robustness (maximizing the mean performance and minimizing the performance variation due to uncertainty) and stochastic model predictive control (ensuring a specific reliability level). Taguchi’s robustness is achieved by introducing a variation term in the control objective to bring a trade-off between mean performance and its variation. TR-DSMPC propagates uncertainty via an approximation method: First-Order Second Moment, which is far more efficient than Monte Carlo-based methods. The uncertainty is considered from two perspectives, time-independent uncertainty by random variables and time-dependent uncertainty by stochastic processes. We compare the proposed method with two other MPC-based methods in terms of safety (spacing error) and efficiency (relative velocity). The results indicate that our proposed method can effectively reduce the performance variation and maintain the mean performance.

Global climate change has caused sea level rise, and one of the most extremely consequences are the increased frequency and hazards of the storm surge disasters, therefore, how to effectively assess the risk of storm surge disaster is of importance to hazard reduction and mitigation. However, the storm surge forecast models have complex parameters, which computational inefficiency. Traditional large-scale assessment usually takes elevation-area method based on GIS software, which result in large errors. The present study is attempted to: (1) Select proper two-dimensional hydraulic storm surge inundation model. This model not only has the physical realism but simple and efficient. (2)The present study will focus on the method to extract the required surface parameters based on the remote sensing data to integrate remote sensing data into the model. This project aims to provide the theoretical basis and methodologies for flood risk assessment in coastal zone of China.

A comparative study has been carried out on the most recent methods for Land Surface Emissivity (LSE) estimation using Landsat TM/ETM+ data. The popularly used method, the integrating NDVI and classification is chosen and analyzed further. The result shows that the estimation model is not accurate enough for LSE is underestimated at either end of the fractional vegetation cover (Pv) range which would lead to overestimating of corresponding Land Surface Temperature (LST). The drawback is modified based on the threshold method, that is, for natural surface and when Pvsoil; for town surface and when Pvm; and for Pv>; 0.5, ε= εv. Finally, a processing way adapting for the improved model in large area is presented and the emissivity model before modification and after improvement is applied to Beijing, China to identify emissivity and further to retrieve LST using image based method. The results show that the large difference between LSE and corresponding LST is located in the town surface and soil with low Pvand that a decrease of emissivity by 0.011058 at 318K will increase LST by about 1K. Thus a promising improvement of comparative accuracy can be expected.

This paper describes a 3D visualization computing methodology to aid the design of tremendous civil engineering in exploiting DEM and RS images. The methodology has been applied to the design and construction of astronomical telescope project, the FAST. Attempting to meet scientific and technological challenges in the process of the project, this paper has developed an innovational 3D computing platform. To simulate the virtual environment of the spot, 3D terrain model has been created based on QuickBird images and DEM, meanwhile, subtle model of massive main active reflector is loaded on the terrain. The algorithm of optimizing telescope antenna location in depressions is presented. In the virtual environment, considering the slope of the terrain, the distribution of feed supporting towers on the telescope has been optimized. It has been proved that the proposed methodology is of high efficiency in the practice of the application.