scholarly journals A Precision Evaluation Method for Remote Sensing Data Sampling Based on Hexagon Discrete Grid

2020 ◽  
Author(s):  
Yue Ma ◽  
Guoqing Li ◽  
Xiaochuang Yao ◽  
Jin Ben ◽  
Qianqian Cao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Evaluation System ◽  
Evaluation Method ◽  
Remote Sensing Data ◽  
Accuracy Evaluation ◽  
Remote Sensing Images ◽  
Sensing Data ◽  
Hexagonal Grids ◽  
Discrete Grid

With the rapid development of earth observation, satellite navigation, mobile communication and other technologies, the order of magnitude of the spatial data we acquire and accumulate is increasing, and higher requirements are put forward for the application and storage of spatial data. Under this circumstance, a new form of spatial data organization emerged-the global discrete grid. This form of data management can be used for the efficient storage and application of large-scale global spatial data, which is a digital multi-resolution the geo-reference model that helps to establish a new model of data association and fusion. It is expected to make up for the shortcomings in the organization, processing and application of current spatial data. There are different types of grid system according to the grid division form, including global discrete grids with equal latitude and longitude, global discrete grids with variable latitude and longitude, and global discrete grids based on regular polyhedrons. However, there is no accuracy evaluation index system for remote sensing images expressed on the global discrete grid to solve this problem. This paper is dedicated to finding a suitable way to express remote sensing data on discrete grids, and establishing a suitable accuracy evaluation system for modeling remote sensing data based on hexagonal grids to evaluate modeling accuracy. The results show that this accuracy evaluation method can evaluate and analyze remote sensing data based on hexagonal grids from multiple levels, and the comprehensive similarity coefficient of the images before and after conversion is greater than 98%, which further proves that the availability hexagonal grid-based remote sensing data of remote sensing images. And among the three sampling methods, the image obtained by the nearest interpolation sampling method has the highest correlation with the original image.

scholarly journals A Precision Evaluation Index System for Remote Sensing Data Sampling Based on Hexagonal Discrete Grids

2021 ◽  
Vol 10 (3) ◽  
pp. 194
Author(s):  
Yue Ma ◽  
Guoqing Li ◽  
Xiaochuang Yao ◽  
Qianqian Cao ◽  
Long Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Evaluation Method ◽  
Remote Sensing Data ◽  
Evaluation Index System ◽  
Accuracy Evaluation ◽  
Remote Sensing Images ◽  
Sensing Data ◽  
Hexagonal Grids ◽  
Discrete Grid

With the rapid development of earth observation, satellite navigation, mobile communication, and other technologies, the order of magnitude of the spatial data we acquire and accumulate is increasing, and higher requirements are put forward for the application and storage of spatial data. As a new form of data management, the global discrete grid can be used for the efficient storage and application of large-scale global spatial data, which is a digital multiresolution georeference model that helps to establish a new model of data association and fusion. It is expected to make up for the shortcomings in the organization, processing, and application of current spatial data. There are different types of grid systems according to the grid division form, including global discrete grids with equal latitude and longitude, global discrete grids with variable latitude and longitude, and global discrete grids based on regular polyhedrons. However, there is no accuracy evaluation index system for remote sensing images expressed on the global discrete grid to solve this problem. This paper is dedicated to finding a suitable way to express remote sensing data on discrete grids, as well as establishing a suitable accuracy evaluation system for modeling remote sensing data based on hexagonal grids to evaluate modeling accuracy. The results show that this accuracy evaluation method can evaluate and analyze remote sensing data based on hexagonal grids from multiple levels, and the comprehensive similarity coefficient of the images before and after conversion is greater than 98%, which further proves the availability of the hexagonal-grid-based remote sensing data of remote sensing images. This evaluation method is generally applicable to all raster remote sensing images based on hexagonal grids, and it can be used to evaluate the availability of hexagonal grid images.

scholarly journals An Efficient Decision Support System for Flood Inundation Management Using Intermittent Remote-Sensing Data

2021 ◽  
Vol 13 (14) ◽  
pp. 2818
Author(s):  
Hai Sun ◽  
Xiaoyi Dai ◽  
Wenchi Shou ◽  
Jun Wang ◽  
Xuejing Ruan
Keyword(s):  
Remote Sensing ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Water Body ◽  
Remote Sensing Data ◽  
Flood Inundation ◽  
Remote Sensing Images ◽  
Sensing Data ◽  
Tracking Model

Timely acquisition of spatial flood distribution is an essential basis for flood-disaster monitoring and management. Remote-sensing data have been widely used in water-body surveys. However, due to the cloudy weather and complex geomorphic environment, the inability to receive remote-sensing images throughout the day has resulted in some data being missing and unable to provide dynamic and continuous flood inundation process data. To fully and effectively use remote-sensing data, we developed a new decision support system for integrated flood inundation management based on limited and intermittent remote-sensing data. Firstly, we established a new multi-scale water-extraction convolutional neural network named DEU-Net to extract water from remote-sensing images automatically. A specific datasets training method was created for typical region types to separate the water body from the confusing surface features more accurately. Secondly, we built a waterfront contour active tracking model to implicitly describe the flood movement interface. In this way, the flooding process was converted into the numerical solution of the partial differential equation of the boundary function. Space upwind difference format and the time Euler difference format were used to perform the numerical solution. Finally, we established seven indicators that considered regional characteristics and flood-inundation attributes to evaluate flood-disaster losses. The cloud model using the entropy weight method was introduced to account for uncertainties in various parameters. In the end, a decision support system realizing the flood losses risk visualization was developed by using the ArcGIS application programming interface (API). To verify the effectiveness of the model constructed in this paper, we conducted numerical experiments on the model's performance through comparative experiments based on a laboratory scale and actual scale, respectively. The results were as follows: (1) The DEU-Net method had a better capability to accurately extract various water bodies, such as urban water bodies, open-air ponds, plateau lakes etc., than the other comparison methods. (2) The simulation results of the active tracking model had good temporal and spatial consistency with the image extraction results and actual statistical data compared with the synthetic observation data. (3) The application results showed that the system has high computational efficiency and noticeable visualization effects. The research results may provide a scientific basis for the emergency-response decision-making of flood disasters, especially in data-sparse regions.

scholarly journals USING SYSTEMS OF PARALLEL AND DISTRIBUTED DATA PROCESSING TO BUILD HYDROLOGICAL MODELS BASED ON REMOTE SENSING DATA

2021 ◽  
Vol XLIII-B4-2021 ◽  
pp. 111-116
Author(s):  
A. A. Kolesnikov ◽  
P. M. Kikin ◽  
E. A. Panidi ◽  
A. G. Rusina
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Spatial Data ◽  
Distributed Processing ◽  
Remote Sensing Data ◽  
Hydroelectric Power Plant ◽  
Point Clouds ◽  
Hydroelectric Power ◽  
Raster Data ◽  
Sensing Data

Abstract. The article describes the possibilities and advantages of using distributed systems in the processing and analysis of remote sensing data. The preparation and processing of various types of remote sensing data (multispectral satellite images, values of climatic indicators, elevation data), which will then be used to build a simulation model of a hydroelectric power plant, was chosen as the basic task for testing the chosen approach. The existing approaches with distributed processing of spatial data of various types (vector cartographic objects, raster data, point clouds, graphs) are analyzed. The description of the developed approach is given and the rationale for the choice of its components is made. The preprocessing operations that were performed on the used raster data are described. An approach to the problems of raster data segmentation based on libraries for distributed machine learning is considered. Comparison of the speed of working with data for various algorithms of machine learning and processing is given.

ANALISIS SPASIAL POLA PERUBAHAN PENGGUNAAN LAHAN PERTANIAN (STUDI KASUS NAGARI CUBADAK)

2019 ◽  
Vol 4 (2) ◽  
pp. 62-68
Author(s):  
Afrital Rezki, S.Pd., M.Si ◽  
Erna Juita ◽  
Dasrizal Dasrizal ◽  
Arie Zella Putra Ulni
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Survey Methods ◽  
Descriptive Analysis ◽  
Remote Sensing Data ◽  
Physical Factors ◽  
Sports Facilities ◽  
Remote Sensing Images ◽  
Sensing Data ◽  
Type Of Land Use

Perkembangan penggunaan tanah secara spasial di Nagari Cubadak dibatasi oleh faktor fisik yang didominasi oleh kemiringan landai dan agak sedikit curam. Penelitian ini dilakukan dengan tujuan untuk  mengetahui dan menganalisis Penggunaan tanah dan Pola perubahan penggunaan tanah untuk pertanian secara spasial di Nagari Cubadak. Penelitian ini menggunakan metode yang dilakukan adalah metode interpretasi citra penginderaan jauh, metode survey, dan analisis deskriptif berbasis keruangan. Interpretasi citra penginderaan jauh dilakukan untuk mengetahui informasi jenis penggunaan lahan khususnya pertanian aktual dan tahun-tahun terdahulu berdasarkan nilai digital yang terekam pada data penginderaan jauh. Dari penelitian ini dapat disimpulkan bahwa, Penggunaan tanah di Nagari Cubadak bisa diklasifikasikan delapan (8) jenis penggunaan lahan yakni; Bangunan Umum, Fasilitas Olahraga, Kolam, Makam, Perumahan, Sawah, Tanah Kosong, Tegalan dan Tempat Ibadah. Kemudian, pengurangan penggunaan tanah 1990–2000 yang paling banyak terdiri dari penggunaan tanah tegalan dengan 91 kavling, paling banyak berubah menjadi perumahan sebanyak 75 kavling, kemudian pengurangan sawah dengan 25 kavling, paling banyak berubah menjadi tegalan dengan 35 kavling dan kolam 20 kavling dengan pengurangan 52 kavling.The development of spatial land use in Nagari Cubadak limited by physical factors which are dominated by sloping slopes and slightly steep. This research was conducted with the aim to find out and analyze land use and the pattern of changes in land use for agriculture spatially in Nagari Cubadak. This study uses the method used is the method of interpretation of remote sensing images, survey methods, and spatial-based descriptive analysis. Interpretation of remote sensing imagery is done to find out information on the type of land use, especially actual and previous years based on digital values recorded on remote sensing data. From this study it can be concluded that, Land use in Nagari Cubadak can be classified as eight (8) types of land use namely; Public Buildings, Sports Facilities, Swimming, Graves, Housing, Paddy Fields, Empty Land, fields and places of worship. Then, the reduction in land use from 1990 to 2000 which mostly consisted of the use of upland land with 91 plots, at most turned into housing lots of 75 plots, then reduced fields with 25 plots, most changed to moor with 35 plots and pools of 20 plots with subtraction 52 lots.

The Dynamic Monitoring on Mining Collapsing around Xinglongzhuang Coal Mine Based on Remote Sensing Images

2013 ◽  
Vol 726-731 ◽  
pp. 4625-4630 ◽  
Author(s):  
Hai Qing Wang ◽  
Ying Jie Zhou ◽  
Ling Chen ◽  
Qing Qing Jing ◽  
Jie Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Economic Development ◽  
Coal Mine ◽  
Local Economic Development ◽  
Remote Sensing Data ◽  
Field Investigation ◽  
Dynamic Monitoring ◽  
Great Contribution ◽  
Remote Sensing Images ◽  
Sensing Data

A large number of old coal mine, such as Xinglongzhuang coal mine, made a great contribution to local economic development and national construction. But, serious mining collapsing was caused also, and local people's livelihood has been affected seriously. The mining collapsing could be identified on remote sensing images by some characteristics. There were 4 period remote sensing data, which was acquired respectively in June 2009, April 2010, June 2011 and July 2012, and field investigation were applied in this articles to study these mining collapsing. The research suggests that the mining collapsing could be divided into the aged type, the middle aged type and the young type. There is a suggestion that, the monitoring and prevention works should be strengthened.

scholarly journals An Optimized Faster R-CNN Method Based on DRNet and RoI Align for Building Detection in Remote Sensing Images

2020 ◽  
Vol 12 (5) ◽  
pp. 762 ◽  
Author(s):  
Tong Bai ◽  
Yu Pang ◽  
Junchao Wang ◽  
Kaining Han ◽  
Jiasai Luo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Region Of Interest ◽  
Remote Sensing Data ◽  
Building Detection ◽  
Remote Sensing Images ◽  
Resource Monitoring ◽  
Residual Network ◽  
Average Precision ◽  
Sensing Data ◽  
Neural Network Algorithm

In recent years, the increase of satellites and UAV (unmanned aerial vehicles) has multiplied the amount of remote sensing data available to people, but only a small part of the remote sensing data has been properly used; problems such as land planning, disaster management and resource monitoring still need to be solved. Buildings in remote sensing images have obvious positioning characteristics; thus, the detection of buildings can not only help the mapping and automatic updating of geographic information systems but also have guiding significance for the detection of other types of ground objects in remote sensing images. Aiming at the deficiency of traditional building remote sensing detection, an improved Faster R-CNN (region-based Convolutional Neural Network) algorithm was proposed in this paper, which adopts DRNet (Dense Residual Network) and RoI (Region of Interest) Align to utilize texture information and to solve the region mismatch problems. The experimental results showed that this method could reach 82.1% mAP (mean average precision) for the detection of landmark buildings, and the prediction box of building coordinates was relatively accurate, which improves the building detection results. Moreover, the recognition of buildings in a complex environment was also excellent.

scholarly journals Mapping Flood-Based Farming Systems with Bayesian Networks

Land ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 369 ◽  
Author(s):  
Issoufou Liman Harou ◽  
Cory Whitney ◽  
James Kung’u ◽  
Eike Luedeling
Keyword(s):  
Remote Sensing ◽  
Bayesian Network ◽  
Spatial Data ◽  
Probabilistic Reasoning ◽  
Expert Knowledge ◽  
Agricultural Research ◽  
Remote Sensing Data ◽  
Farming Systems ◽  
Spatially Explicit ◽  
Sensing Data

Many actors in agricultural research, development, and policy arenas require accurate information on the spatial extents of cropping and farming practices. While remote sensing provides ways for obtaining such information, it is often difficult to distinguish between different types of agricultural practices or identify particular farming systems. Stochastic system behavior or similarity in the spectral signatures of different system components can lead to misclassification. We addressed this challenge by using a probabilistic reasoning engine informed by expert knowledge and remote sensing data to map flood-based farming systems (FBFS) across Kisumu County in Kenya and the Tigray region in Ethiopia. Flood-based farming is an important form of agricultural production employed in regions with seasonal water surplus, which can be harvested and used to irrigate crops. Geographic settings for FBFS vary widely in terms of hydrology, vegetation, and local practices of agronomic flooding. Agronomic success is often difficult to anticipate, because the timing and amount of flooding usually cannot be precisely predicted. We generated a Bayesian network model to describe the FBFS settings of the study regions. We acquired three years (2014–2016) of Moderate Resolution Imaging Spectroradiometer (MODIS) Terra spectral data as eight-day composite time series and elevation data from the Shuttle Radar Topography Mission (SRTM) to compute 10 spatial data metrics corresponding to 10 of the 17 Bayesian network nodes. We used the spatial data metrics in a fully probabilistic framework to generate the 10 spatial data nodes. We then used these as inputs for the probabilistic model to generate prior and posterior spatial estimates for specific metrics along with their spatially explicit uncertainties. We show how such an approach can be used to predict plausible areas for FBFS based on several scenarios. We demonstrate how spatially explicit information can be derived from remote sensing data as fuzzy quantifiers for incorporating uncertainties when mapping complex systems. The approach achieved a remarkably accurate result in both study areas, where 84–90% of various FBFS fields sampled were correctly mapped as having a high chance of being suitable for the practice.

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