Land Usage Analysis: A Machine Learning Approach

Ali, Karamat; Rahman, Hidayat Ur; Khan, Rehanullah; Yasir, Siddiqui Muhammad; Najeeb, Fahad; Amin, Sadiq; Khan, Rafaqat Alam; Manzoor, Sanaullah; Chaudhry, Nadeem

doi:10.5120/ijca2016909936

Cited by 3 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sb for Norway and Mn for Serbia were correctly predicted by our model. [8] Khan, S. H., et al [2017] there are a number of reasons why monitoring land cover change is critical to regional resource management and catastrophe response. Images taken by satellites over a period of 29 years are analysed in this article to determine how forest cover has changed over time .…”

Section: Literature Surveymentioning

confidence: 99%

Prediction of Housing Price and Forest Cover Using Mosaics with Uncertain Satellite Imagery

Sai

Rao

Vani

et al. 2022

IJRITCC

View full text Add to dashboard Cite

The growing world is more expensive to estimate land use, road length, and forest cover using a plant-scaled ground monitoring system. Satellite imaging contains a significant amount of detailed uncertain information. Combining this with machine learning aids in the organization of these data and the estimation of each variable separately. The resources necessary to deploy Machine learning technologies for Remote sensing images, on the other hand, restrict their reach ability and application. Based on satellite observations which are notably underutilised in impoverished nations, while practical competence to implement SIML might be restricted. Encoded forms of images are shared across tasks, and they will be calculated and sent to an infinite number of researchers who can achieve top-tier SIML performance by training a regression analysis onto the actual data. By separating the duties, the proposed SIML solution, MOSAIKS, shapes SIML approachable and global. A Featurization stage turns remote sensing data into concise vector representations, and a regression step makes it possible to learn the correlations which are specific to its particular task which link the obtained characteristics to the set of uncertain data.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Prediction of Housing Price and Forest Cover Using Mosaics with Uncertain Satellite Imagery

Sai

Rao

Vani

et al. 2022

IJRITCC

View full text Add to dashboard Cite

show abstract

“…A suite of machine learning classifiers are available for image analysis such as Naive Bayes, Support Vector Machine (SVM), Classification and Regression Trees (CART), Random Forest (RF), Gradient Boosting Machine (GBM), Neural Network (NN), and others. They have become increasingly popular within the field of remote sensing in recent years due to their applicability across large datasets and their ability to generate more accurate and consistent results (Yuan et al, 2009;Ali et al, 2016;Godinho et al, 2016;Kussui et al, 2016;Ming et al, 2016;Rogers et al, 2017;Shelestov et al, 2017;Xiong et al, 2017;Bunting et al, 2018;Gauci et al, 2018;Karakizi et al, 2018;Maxwell et al, 2018;Liu et al, 2019). Machine learning algorithms such as maximum-likelihood are well established within the field, but an increasing number of non-parametric classifiers have emerged, providing decision trees, plane fitting, clustering and deep-learning algorithms such as neural networks.…”

Section: Introductionmentioning

confidence: 99%

Large-Scale High-Resolution Coastal Mangrove Forests Mapping Across West Africa With Machine Learning Ensemble and Satellite Big Data

Xue

Fatoyinbo²,

Thomas

et al. 2021

Front. Earth Sci.

View full text Add to dashboard Cite

Coastal mangrove forests provide important ecosystem goods and services, including carbon sequestration, biodiversity conservation, and hazard mitigation. However, they are being destroyed at an alarming rate by human activities. To characterize mangrove forest changes, evaluate their impacts, and support relevant protection and restoration decision making, accurate and up-to-date mangrove extent mapping at large spatial scales is essential. Available large-scale mangrove extent data products use a single machine learning method commonly with 30 m Landsat imagery, and significant inconsistencies remain among these data products. With huge amounts of satellite data involved and the heterogeneity of land surface characteristics across large geographic areas, finding the most suitable method for large-scale high-resolution mangrove mapping is a challenge. The objective of this study is to evaluate the performance of a machine learning ensemble for mangrove forest mapping at 20 m spatial resolution across West Africa using Sentinel-2 (optical) and Sentinel-1 (radar) imagery. The machine learning ensemble integrates three commonly used machine learning methods in land cover and land use mapping, including Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Network (NN). The cloud-based big geospatial data processing platform Google Earth Engine (GEE) was used for pre-processing Sentinel-2 and Sentinel-1 data. Extensive validation has demonstrated that the machine learning ensemble can generate mangrove extent maps at high accuracies for all study regions in West Africa (92%–99% Producer’s Accuracy, 98%–100% User’s Accuracy, 95%–99% Overall Accuracy). This is the first-time that mangrove extent has been mapped at a 20 m spatial resolution across West Africa. The machine learning ensemble has the potential to be applied to other regions of the world and is therefore capable of producing high-resolution mangrove extent maps at global scales periodically.

show abstract

Deep Learning Methods for Land Cover and Land Use Classification in Remote Sensing: A Review

Alem

Kumar

2020

2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)

View full text Add to dashboard Cite

Land Usage Analysis: A Machine Learning Approach

Cited by 3 publications

References 16 publications

Prediction of Housing Price and Forest Cover Using Mosaics with Uncertain Satellite Imagery

Prediction of Housing Price and Forest Cover Using Mosaics with Uncertain Satellite Imagery

Large-Scale High-Resolution Coastal Mangrove Forests Mapping Across West Africa With Machine Learning Ensemble and Satellite Big Data

Deep Learning Methods for Land Cover and Land Use Classification in Remote Sensing: A Review

Contact Info

Product

Resources

About