scholarly journals A Review of the Machine Learning in GIS for Megacities Application

2020 ◽  
Author(s):  
Nasim Tohidi ◽  
Rustam B. Rustamov
Keyword(s):  
Machine Learning ◽  
Data Processing ◽  
Driving Forces ◽  
Information Filtering ◽  
Remote Sensing Data ◽  
Urban Structure ◽  
Geospatial Information ◽  
Technology Application ◽  
Future Events ◽  
Images Processing

Machine learning (ML) is very useful for analyzing data in many domains, including the satellite images processing. In the remote sensing data processing, ML tools are mainly founded out a place for filtering, interpretation and prediction information. Filtering aims at removing noise and performing transformations, which is vital segment of data processing as useful performance of data validation. An interpretation is significant part of it as the stage of objects classification depends of existing task for solution. Prediction is performed to estimate precise values of underlying parameters or future events in the data. It can be used successfully above achievements in a variety of areas. An urbanization is one of the spheres of advance technology application where highly need to collect appropriate data for understanding of challenges facing society. The process of urbanization becomes very important problem, thanks to city expansion. Each city is a complicated system. It consists of various interactive sub-systems and is affected by multiple factors, including population growth, transportation and management policies. To understand the driving forces of the urban structure change, the satellite-based estimates are considered to monitor these changes, in long term. GIS (geographic information system) is equivalent to methods related to the use of geospatial information. Besides, the increasing application of ML techniques in various fields, including GIS, is undeniable. Thus, the chapter attempts to review the application of ML techniques in GIS with a focus on megacities and theirs features fixing/identification and solution.

scholarly journals TRADITIONAL AND MODERN METHODS OF SATELLITE IMAGES PROCESSING FOR OPERATIONAL MAPPING OF FOREST COVER DISTUBANCES

2020 ◽  
Vol 25 (3) ◽  
pp. 201-213
Author(s):  
Andrey V. Tarasov ◽  
Keyword(s):  
Machine Learning ◽  
Forest Management ◽  
Forest Cover ◽  
Forest Disturbance ◽  
Remote Sensing Data ◽  
Machine Learning Algorithms ◽  
Forest Disturbances ◽  
Detection Algorithms ◽  
Vegetation Indexes ◽  
Images Processing

Real-time mapping of forest disturbances is important for forest management. Detection of forest stands damaged by natural or human-induced factors allows making immediate necessary management decisions. To implement such a management strategy, it is necessary to use the methods of operational mapping. With the advent of the Earth remote sensing data (RSD), which have high spatial and temporal resolution (Planet Scope and Sentinel-2), it becomes possible to implement modern operational mapping methods for forest management operations (particularly, forest disturbance detection). Since the monitoring area and the number of images sharply increases, the need for automated image processing methods also rises. This paper provides an overview of “traditional methods” for identifying forest cover disturbances (vegetation indexes, Tasseled Cap, multiband and single band change detection etc), their basis, limitations, and experience of their application in Russia and in the world. Instead, algorithm based on machine learning methods and their classification are presented. Benefits and limitations of both groups of forest disturbances detection algorithms are noted. In addition, it was found out that there is limited experience of application of machine learning algorithms for RSD processing and such kind of research is relevant.

CLASS-BASED MACHINE LEARNING FOR NEXT GENERATION WELLBORE DATA PROCESSING AND INTERPRETATION

2019 ◽  
Author(s):  
Vikas Jain ◽  
Po-Yen Wu ◽  
Ridvan Akkurt ◽  
Brook Hodenfield ◽  
Tianmin Jiang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Processing ◽  
Next Generation

scholarly journals Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine

2021 ◽  
Vol 13 (8) ◽  
pp. 1433
Author(s):  
Shobitha Shetty ◽  
Prasun Kumar Gupta ◽  
Mariana Belgiu ◽  
S. K. Srivastav
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Random Sampling ◽  
Sampling Design ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Multi Temporal ◽  
Google Earth Engine

Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires understanding the main factors influencing their performance. The present study investigated firstly the effect of training sampling design on the classification results obtained by Random Forest (RF) classifier and, secondly, it compared its performance with other machine learning classifiers for LULC mapping using multi-temporal satellite remote sensing data and the Google Earth Engine (GEE) platform. We evaluated the impact of three sampling methods, namely Stratified Equal Random Sampling (SRS(Eq)), Stratified Proportional Random Sampling (SRS(Prop)), and Stratified Systematic Sampling (SSS) upon the classification results obtained by the RF trained LULC model. Our results showed that the SRS(Prop) method favors major classes while achieving good overall accuracy. The SRS(Eq) method provides good class-level accuracies, even for minority classes, whereas the SSS method performs well for areas with large intra-class variability. Toward evaluating the performance of machine learning classifiers, RF outperformed Classification and Regression Trees (CART), Support Vector Machine (SVM), and Relevance Vector Machine (RVM) with a >95% confidence level. The performance of CART and SVM classifiers were found to be similar. RVM achieved good classification results with a limited number of training samples.

scholarly journals Machine learning for perovskite materials design and discovery

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Qiuling Tao ◽  
Pengcheng Xu ◽  
Minjie Li ◽  
Wencong Lu
Keyword(s):  
Machine Learning ◽  
Technological Innovation ◽  
Future Development ◽  
Rational Design ◽  
Driving Forces ◽  
Scientific Progress ◽  
Double Perovskites ◽  
Perovskite Materials ◽  
Development Tendency ◽  
Development Prospects

AbstractThe development of materials is one of the driving forces to accelerate modern scientific progress and technological innovation. Machine learning (ML) technology is rapidly developed in many fields and opening blueprints for the discovery and rational design of materials. In this review, we retrospected the latest applications of ML in assisting perovskites discovery. First, the development tendency of ML in perovskite materials publications in recent years was organized and analyzed. Second, the workflow of ML in perovskites discovery was introduced. Then the applications of ML in various properties of inorganic perovskites, hybrid organic–inorganic perovskites and double perovskites were briefly reviewed. In the end, we put forward suggestions on the future development prospects of ML in the field of perovskite materials.

Machine Learning Migration for Efficient Near-Data Processing

2021 ◽  
Author(s):  
Aline S. Cordeiro ◽  
Sairo R. dos Santos ◽  
Francis B. Moreira ◽  
Paulo C. Santos ◽  
Luigi Carro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Processing

scholarly journals Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4408
Author(s):  
Iman Salehi Hikouei ◽  
S. Sonny Kim ◽  
Deepak R. Mishra
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Salt Marsh ◽  
Bulk Density ◽  
Remote Sensing Data ◽  
Soil Bulk Density ◽  
Soil Samples ◽  
Marsh Soil ◽  
Landsat 7 ◽  
Salt Marsh Soil

Remotely sensed data from both in situ and satellite platforms in visible, near-infrared, and shortwave infrared (VNIR–SWIR, 400–2500 nm) regions have been widely used to characterize and model soil properties in a direct, cost-effective, and rapid manner at different scales. In this study, we assess the performance of machine-learning algorithms including random forest (RF), extreme gradient boosting machines (XGBoost), and support vector machines (SVM) to model salt marsh soil bulk density using multispectral remote-sensing data from the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) platform. To our knowledge, use of remote-sensing data for estimating salt marsh soil bulk density at the vegetation rooting zone has not been investigated before. Our study reveals that blue (band 1; 450–520 nm) and NIR (band 4; 770–900 nm) bands of Landsat-7 ETM+ ranked as the most important spectral features for bulk density prediction by XGBoost and RF, respectively. According to XGBoost, band 1 and band 4 had relative importance of around 41% and 39%, respectively. We tested two soil bulk density classes in order to differentiate salt marshes in terms of their capability to support vegetation that grows in either low (0.032 to 0.752 g/cm3) or high (0.752 g/cm3 to 1.893 g/cm3) bulk density areas. XGBoost produced a higher classification accuracy (88%) compared to RF (87%) and SVM (86%), although discrepancies in accuracy between these models were small (<2%). XGBoost correctly classified 178 out of 186 soil samples labeled as low bulk density and 37 out of 62 soil samples labeled as high bulk density. We conclude that remote-sensing-based machine-learning models can be a valuable tool for ecologists and engineers to map the soil bulk density in wetlands to select suitable sites for effective restoration and successful re-establishment practices.

Improved t-SNE based manifold dimensional reduction for remote sensing data processing

2018 ◽  
Vol 78 (4) ◽  
pp. 4311-4326 ◽  
Author(s):  
Weijing Song ◽  
Lizhe Wang ◽  
Peng Liu ◽  
Kim-Kwang Raymond Choo
Keyword(s):  
Remote Sensing ◽  
Data Processing ◽  
Dimensional Reduction ◽  
Remote Sensing Data ◽  
Sensing Data
Sign in / Sign up

Export Citation Format

Share Document