Climate Engine: Cloud Computing and Visualization of Climate and Remote Sensing Data for Advanced Natural Resource Monitoring and Process Understanding

2017 ◽  
Vol 98 (11) ◽  
pp. 2397-2410 ◽  
Author(s):  
Justin L. Huntington ◽  
Katherine C. Hegewisch ◽  
Britta Daudert ◽  
Charles G. Morton ◽  
John T. Abatzoglou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Big Data ◽  
Natural Resource ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Resource Monitoring ◽  
Sensing Data ◽  
Process Understanding ◽  
Natural Resource Monitoring

Abstract The paucity of long-term observations, particularly in regions with heterogeneous climate and land cover, can hinder incorporating climate data at appropriate spatial scales for decision-making and scientific research. Numerous gridded climate, weather, and remote sensing products have been developed to address the needs of both land managers and scientists, in turn enhancing scientific knowledge and strengthening early-warning systems. However, these data remain largely inaccessible for a broader segment of users given the computational demands of big data. Climate Engine (http://ClimateEngine.org) is a web-based application that overcomes many computational barriers that users face by employing Google’s parallel cloud-computing platform, Google Earth Engine, to process, visualize, download, and share climate and remote sensing datasets in real time. The software application development and design of Climate Engine is briefly outlined to illustrate the potential for high-performance processing of big data using cloud computing. Second, several examples are presented to highlight a range of climate research and applications related to drought, fire, ecology, and agriculture that can be rapidly generated using Climate Engine. The ability to access climate and remote sensing data archives with on-demand parallel cloud computing has created vast opportunities for advanced natural resource monitoring and process understanding.

Proof of concept of a novel cloud computing approach for object-based remote sensing data analysis and classification

2018 ◽  
Vol 56 (4) ◽  
pp. 536-553 ◽  
Author(s):  
R. R. Antunes ◽  
T. Blaschke ◽  
D. Tiede ◽  
E. S. Bias ◽  
G. A. O. P. Costa ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Data Analysis ◽  
Remote Sensing Data ◽  
Proof Of Concept ◽  
Sensing Data ◽  
Object Based ◽  
Computing Approach

scholarly journals Study of energy-efficient architecture address utilizing topography and geomorphology based on Google Earth and its remote sensing data

2020 ◽  
Vol 206 ◽  
pp. 01020
Author(s):  
Yijin Wang
Keyword(s):  
Remote Sensing ◽  
Energy Saving ◽  
City Planning ◽  
Smart Cities ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Green Energy ◽  
Rational Analysis ◽  
Sensing Data ◽  
Distribution Features

In this paper, we describe the formatting guidelines for ACM SIG Proceedings. With the development of social economy, smart cities, especially green energy-saving buildings, are foremost trend in the future. The location of green buildings has a very important impact on the design and plan of future smart cities. The influence of the natural environment, especially that of the topography and landform on the location of architectural design is very significant. Google Earth (GE) platform can provide sufficient remote sensing data, which greatly interpret and promote surface information. However, just few people have done related research. This article takes Beijing as an example and uses Google Earth platform and the remote sensing data to obtain the 3D digital elevation model (DEM) data; and then Google earth’s geomorphology data are used to analyze the landform features. Finally, by analyzing their characteristics and distribution features, five energy-saving building locations were selected in Beijing. It can be concluded that GE, is an effective and potential platform for providing remote sensing data, and analyzing the DEM and landform. The rational analysis of the building addresses in this paper could help the buildings to avoid potential geological disasters and make full use of natural resources. Moreover, this research on energyefficient building addresses make a suggestion for future smart city planning.

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.

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