Implementation of Ensemble Deep Learning Coupled with Remote Sensing for the Quantitative Analysis of Changes in Arable Land Use in a Mining Area

Ji, Haowei; Luo, Xianqi

doi:10.1007/s12524-021-01430-6

Cited by 6 publications

(2 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Boulesteix et al [33], in the field of bioinformatics, successfully implemented the use of RF and SVM classifiers to improve crop classification accuracy and to provide spatial information on map uncertainty. With the advancement of remote sensing (RS) and deep learning techniques, many methods for revealing land-use changes are accessible [34][35][36][37]. In the field of geological modeling, Abert and Ammar [38] applied RF classification and RS in geological mapping in the Jebel Meloussi area.…”

Section: Random Forest Techniquementioning

confidence: 99%

Application of Remote Sensing and Geographic Information System Technologies to Assess the Impact of Mining: A Case Study at Emalahleni

Cudjoe,

Kwarteng,

Anning

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

This article presents an assessment of the impact of mining activities in the Emalahleni municipality, using GIS and RS technologies. The random forest algorithm was used to classify the land use and land cover in the Emalahleni municipality over a three-decade period (1990–2020). The classifications are settlement, water, mining area, vegetation, and bare land. The majority of the study area was found to be rocky ground, accounting for approximately 60% of the total study area. Change detection maps were created for vegetation and mining to assess the extent of land degradation in the study area over the three-decade period. The findings in this study highlight the importance of understanding the changes in land use and vegetation cover in the study area and its impact on the environment, as well as the local community. It is crucial to develop sustainable land management strategies that ensure that a reasonable balance concerning the economic development activities is achieved, such as mining with environmental management for its long-term viability for future generations. The data presented in this study provides a useful baseline for further research and can inform land-use planning and decision-making processes in Emalahleni.

show abstract

Section: Random Forest Techniquementioning

confidence: 99%

Application of Remote Sensing and Geographic Information System Technologies to Assess the Impact of Mining: A Case Study at Emalahleni

Cudjoe,

Kwarteng,

Anning

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…With the development of remote sensing (RS) and deep learning (DL) technologies, various forms of data for detecting land use changes are available. e goal of Ji and Luo is to promote the coordinated development of land use [5]. Remote sensing data can be used to improve understanding of environmental changes over longer periods of time.…”

Section: Related Workmentioning

confidence: 99%

Changes of Remote Sensing Data in Urban Buildings Based on Neural Network Algorithm

Xue

Meng

2022

Mobile Information Systems

View full text Add to dashboard Cite

Remote sensing data refer to remote sensing data sets with typical big data characteristics obtained by various remote sensing technologies. Based on this understanding, massive remote sensing datasets are the main method, and auxiliary data from other sources are integrated. The DN value (digital number) is the brightness value of the remote sensing image pixel and the gray value of the recorded ground objects. Unitless is an integer value. The value is related to the sensor’s radiation resolution, ground object emissivity, atmospheric transmittance, and scattering rate. This study aims to study the neural network algorithm for remote sensing data changes in urban buildings. This study deliberately goes to Tianjin Binhai New Area, taking this as an example to analyze and study the change of construction land. It uses satellite remote sensing data as a basis to analyze the trend of its building land. This study proposes which factors lead to the decline of urban construction land in Tianjin Binhai New Area. Finally, through experimental research and analysis, it is found that from 1996 to 2016, the registered population of Tianjin Binhai New Area has increased a lot, accounting for 26.49% of the total population. The number of urban employees increased by 956,500. This is also the leading factor in the direction of the city for the building.

show abstract

Deep learning implementations in mining applications: a compact critical review

et al. 2023

View full text Add to dashboard Cite

Deep learning is a sub-field of artificial intelligence that combines feature engineering and classification in one method. It is a data-driven technique that optimises a predictive model via learning from a large dataset. Digitisation in industry has included acquisition and storage of a variety of large datasets for interpretation and decision making. This has led to the adoption of deep learning in different industries, such as transportation, manufacturing, medicine and agriculture. However, in the mining industry, the adoption and development of new technologies, including deep learning methods, has not progressed at the same rate as in other industries. Nevertheless, in the past 5 years, applications of deep learning have been increasing in the mining research space. Deep learning has been implemented to solve a variety of problems related to mine exploration, ore and metal extraction and reclamation processes. The increased automation adoption in mining provides an avenue for wider application of deep learning as an element within a mine automation framework. This work provides a compact, comprehensive review of deep learning implementations in mining-related applications. The trends of these implementations in terms of years, venues, deep learning network types, tasks and general implementation, categorised by the value chain operations of exploration, extraction and reclamation are outlined. The review enables shortcomings regarding progress within the research context to be highlighted such as the proprietary nature of data, small datasets (tens to thousands of data points) limited to single operations with unique geology, mine design and equipment, lack of large scale publicly available mining related datasets and limited sensor types leading to the majority of applications being image-based analysis. Gaps identified for future research and application includes the usage of a wider range of sensor data, improved understanding of the outputs by mining practitioners, adversarial testing of the deep learning models, development of public datasets covering the extensive range of conditions experienced in mines.

show abstract

Implementation of Ensemble Deep Learning Coupled with Remote Sensing for the Quantitative Analysis of Changes in Arable Land Use in a Mining Area

Cited by 6 publications

References 34 publications

Application of Remote Sensing and Geographic Information System Technologies to Assess the Impact of Mining: A Case Study at Emalahleni

Application of Remote Sensing and Geographic Information System Technologies to Assess the Impact of Mining: A Case Study at Emalahleni

Changes of Remote Sensing Data in Urban Buildings Based on Neural Network Algorithm

Deep learning implementations in mining applications: a compact critical review

Contact Info

Product

Resources

About