4.6 Review

Land Use and Land Cover Mapping in the Era of Big Data

Related references

Note: Only part of the references are listed.
Review Computer Science, Information Systems

Computer vision models for comparing spatial patterns: understanding spatial scale

Karim Malik et al.

Summary: This paper reviews the use of computer vision models and artificial neural networks in geographical analysis, with a focus on the representation and comparison of spatial patterns. The authors find that scale, which is typically considered a model parameter in computer vision, is a contextual element in geographical research. However, convolutional neural networks (CNNs) are relatively robust to small-scale variations due to their ability to learn multiscale features. Although there are still challenges in parameterizing computer vision models to represent multiscale patterns, a typology of scales can provide a framework for guidelines in a geographic context.

INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE (2023)

Review Computer Science, Information Systems

Deep learning for processing and analysis of remote sensing big data: a technical review

Xin Zhang et al.

Summary: The article outlines the characteristics of remote sensing big data and provides a detailed review on how deep learning has been applied to the processing and analysis of remote sensing data, covering various tasks with specific technical details.

BIG EARTH DATA (2022)

Review Environmental Sciences

Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects

Junye Wang et al.

Summary: Land-use and land-cover change (LULCC) is important in natural resource management, environmental modelling, and agricultural production management. However, LULCC detection and modelling is a complex process in remote sensing due to the large amount of data involved and the need to consider different factors. Machine learning has not yet had a significant impact on LULCC modelling, especially in predicting urbanization and crop yields, because land cover types are dynamic at a local scale and influenced by various natural and human factors. Challenges in using machine learning for LULCC modelling include detecting and predicting LULC evolutionary processes, considering the applicability and feasibility of different approaches, and incorporating local ecological, hydrological, and socio-economic drivers into the analysis.

SCIENCE OF THE TOTAL ENVIRONMENT (2022)

Article Environmental Sciences

Monitoring Annual Land Use/Land Cover Change in the Tucson Metropolitan Area with Google Earth Engine (1986-2020)

Fabrice Dubertret et al.

Summary: This article introduces how to monitor the land use and land cover changes in the Tucson metropolitan area of Arizona, USA, using various data and techniques. It also shows that policies to reduce urban sprawl in the area have limited effects. Additionally, an automated tool is provided for future monitoring of changes.

REMOTE SENSING (2022)

Article Environmental Studies

Combining Tabular and Satellite-Based Datasets to Better Understand Cropland Change

Kenneth Lee Copenhaver

Summary: In recent years, regulatory agencies in the USA and Europe have required documentation to ensure that land used for crop and biofuel production has not been converted from carbon-capturing grasslands or forests. However, accurately measuring these land cover changes has been challenging. This study analyzed satellite datasets, tabular datasets, and aerial imagery to identify potential locations of land use change more accurately. The results suggest that long-term historical land cover/land use analysis could help regulatory agencies measure the impacts of conversion of natural lands to crops more accurately.
Review Environmental Sciences

Land use/land cover in view of earth observation: data sources, input dimensions, and classifiers-a review of the state of the art

Prem Chandra Pandey et al.

Summary: The concept of land use/land cover (LULC) is crucially linked to various aspects of the human and physical environment, and Earth observation (EO) technology has greatly enhanced our ability to classify land cover with high spatial and spectral resolution imagery. Increasing use of EO sensors has opened up new applications in different disciplines, emphasizing the importance of understanding the spatial-spectral-temporal characteristics of satellite data and classification approaches. Research in this field will need to focus on integrating techniques used in LULC mapping to gain a more comprehensive understanding at regional or global scales.

GEOCARTO INTERNATIONAL (2021)

Article Computer Science, Information Systems

Mapping essential urban land use categories (EULUC) using geospatial big data: Progress, challenges, and opportunities

Bin Chen et al.

Summary: Urban land use information is crucial for urban planning, environmental management, and biodiversity conservation. Maps outlining essential urban land use categories have enabled various applications, with advances in Earth observations, algorithms, and data sources offering great potential for fine-resolution mapping. However, challenges such as sample collection and mixed land use remain in current studies.

BIG EARTH DATA (2021)

Article Geography, Physical

A comparison of the integrated fuzzy object-based deep learning approach and three machine learning techniques for land use/cover change monitoring and environmental impacts assessment

Bakhtiar Feizizadeh et al.

Summary: The study developed an integrated approach combining fuzzy object-based image analysis and deep learning (FOBIA-DL) for monitoring land use/cover (LULC) and changes, which outperformed other machine learning algorithms with high accuracy and spatial certainty. The combination of Fuzzy-OBIA and DCNNs improved decision rules and spatial accuracy, contributing to the advancements in image analysis and classification. The research supports environmental initiatives such as drought mitigation, land use management, and precision agriculture programs by providing valuable insights for decision-makers and authorities.

GISCIENCE & REMOTE SENSING (2021)

Article Geochemistry & Geophysics

Deep Subpixel Mapping Based on Semantic Information Modulated Network for Urban Land Use Mapping

Da He et al.

Summary: The mixed pixel problem is common in urban land use interpretation in remote sensing images due to hardware limitations. Subpixel mapping is a common approach to solve this problem, while deep learning-based subpixel mapping network has been recently proposed for finer mapping. The article introduces a semantic information modulated (SIM) deep subpixel mapping network (SIMNet), which uses low-resolution semantic images as prior to enhance spatial context features representation.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

Review Remote Sensing

Integrating remote sensing and geospatial big data for urban land use mapping: A review

Jiadi Yin et al.

Summary: Remote Sensing has been used in urban mapping for a long time, however, the complexity and diversity of urban functional patterns are difficult to be captured by RS only. Emerging Geospatial Big Data are considered as the supplement to RS data, and help to contribute to our understanding of urban lands from physical aspects to socioeconomic aspects. The integration of RS and GBD features were categorized into feature-level integration and decision-level integration in urban land use classification.

INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION (2021)

Article Remote Sensing

Identifying core driving factors of urban land use change from global land cover products and POI data using the random forest method

Hao Wu et al.

Summary: This study proposed a margin-based measure of random forest to identify the core driving factors of urban land use change, which was found to be more reliable and sensitive in detecting the driving mechanism behind land use change. Regardless of the similarity measure chosen and applied, the importance values and ranking orders of driving factors measured by the margin-based method were stable.

INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION (2021)

Article Chemistry, Analytical

Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative Study

Raoof Naushad et al.

Summary: The study implemented transfer learning to fine-tune VGG16 and WRNs networks for LULC classification using the EuroSAT dataset, achieving high accuracy while addressing the limited-data problem. Techniques such as early stopping, gradient clipping, adaptive learning rates, and data augmentation were used to optimize performance and computational time. The proposed approach based on WRNs outperformed previous methods in terms of computational efficiency and accuracy, achieving 99.17% accuracy.

SENSORS (2021)

Article Geochemistry & Geophysics

OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing

John E. Vargas-Munoz et al.

Summary: OpenStreetMap (OSM) is a community-based, freely available, editable map service with heterogeneous completeness and quality due to volunteer editors with different mapping skills. Despite this, OSM is widely used in geosciences, Earth observation, and environmental sciences. Recent methods based on machine learning aim to improve and utilize OSM data, either by enhancing coverage and quality of OSM layers or by training models using image data for applications such as navigation and land use classification.

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE (2021)

Article Environmental Sciences

Land use/land cover (LULC) analysis (2009-2019) with Google Earth Engine and 2030 prediction using Markov-CA in the Rondonia State, Brazil

Isabela Xavier Floreano et al.

Summary: The study evaluated land use/land cover changes in Rondonia state over the past ten years and predicted future changes. The results showed a 15.7% reduction in forested areas, with a prediction that by 2030, around 30% of remaining forests will be logged and converted into occupied areas. These findings highlight the importance of measures and policies integrated with investments in research and satellite monitoring to reduce deforestation in the Brazilian Amazon and preserve its crucial role in addressing climate change.

ENVIRONMENTAL MONITORING AND ASSESSMENT (2021)

Article Environmental Sciences

Decision-Level and Feature-Level Integration of Remote Sensing and Geospatial Big Data for Urban Land Use Mapping

Jiadi Yin et al.

Summary: This study summarized the methods of integrating remote sensing and geospatial big data (GBD) and evaluated them through a case study of urban land use mapping in Hangzhou, China. The results showed that decision-level integration (DI) generally outperforms feature-level integration (FI) in classification, and a combination of the two methods can improve urban land use mapping.

REMOTE SENSING (2021)

Article Geochemistry & Geophysics

Accurate Estimation of the Proportion of Mixed Land Use at the Street-Block Level by Integrating High Spatial Resolution Images and Geospatial Big Data

Jialyu He et al.

Summary: This article proposed an end-to-end two-stream convolutional neural network (CNN) combining high spatial resolution images and geospatial big data to estimate the proportion of mixed land use. Two deep learning networks were used to construct the CF-CNN model, achieving higher classification accuracy compared to single-source data methods. The Shannon diversity index was applied to quantify urban mixed land use, with Spearman correlation coefficients calculated to verify the effectiveness of the mixed land use composition.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

Article Geography, Physical

Mapping essential urban land use categories with open big data: Results for five metropolitan areas in the United States of America

Bin Chen et al.

Summary: This study proposed a robust and cost-effective framework for mapping urban land use categories using openly available multi-source geo-spatial big data at block level, achieving high accuracies. The multi-layer stacking ensemble models showed better performance in urban land use classification with high-dimensional features compared to base models, highlighting the importance of data sources and methods in mapping urban land use categories.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2021)

Review Environmental Sciences

Hyperspectral and Lidar Data Applied to the Urban Land Cover Machine Learning and Neural-Network-Based Classification: A Review

Agnieszka Kuras et al.

Summary: The combination of hyperspectral and lidar systems has shown promising results in mapping urban environments, with machine learning algorithms playing a crucial role in urban land cover classification. However, challenges remain in extracting key features and managing computational expenses.

REMOTE SENSING (2021)

Review Environmental Studies

A Synthesis of Land Use/Land Cover Studies: Definitions, Classification Systems, Meta-Studies, Challenges and Knowledge Gaps on a Global Landscape

Ryan Nedd et al.

Summary: Land use/land cover change (LULCC) has a significant impact on land-atmosphere/climate interactions, with main reasons being rapid population growth, migration, and rural-to-urban conversion. Past studies have focused on LULC definitions, classification systems, direct and indirect changes, challenges, and knowledge gaps. Challenges in LULC include data consistency and quality, while knowledge gaps exist in ecosystem services, forestry, and data/image modeling in LULC.
Article Geography

Sensing Mixed Urban Land-Use Patterns Using Municipal Water Consumption Time Series

Qingfeng Guan et al.

Summary: This study proposed a framework using municipal water consumption data to delineate and analyze mixed land-use patterns and their evolution. The two-step classification strategy and diversity index were used to differentiate and measure different socioeconomic functions of land use. Taking Changshu in China as an example, the results demonstrated the spatial expansion and intensification of urbanization, as well as the increasing degree of land-use mixture with urban growth.

ANNALS OF THE AMERICAN ASSOCIATION OF GEOGRAPHERS (2021)

Article Environmental Sciences

Mapping human's digital footprints on the Tibetan Plateau from multi-source geospatial big data

Jiawei Yi et al.

SCIENCE OF THE TOTAL ENVIRONMENT (2020)

Review Environmental Sciences

Survey on Land Use/Land Cover (LU/LC) change analysis in remote sensing and GIS environment: Techniques and Challenges

Sam Navin MohanRajan et al.

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2020)

Editorial Material Multidisciplinary Sciences

Land cover mapping toward finer scales

Min Feng et al.

SCIENCE BULLETIN (2020)

Review Environmental Sciences

Impact of land use change on ecosystem services: A review

Shaikh Shamim Hasan et al.

ENVIRONMENTAL DEVELOPMENT (2020)

Article Computer Science, Interdisciplinary Applications

Land use land cover mapping using advanced machine learning classifiers: A case study of Shiraz city, Iran

Ali Jamali

EARTH SCIENCE INFORMATICS (2020)

Editorial Material Environmental Sciences

A summary of the special issue on remote sensing of land change science with Google earth engine

Le Wang et al.

REMOTE SENSING OF ENVIRONMENT (2020)

Article Computer Science, Information Systems

Semantic Integration of Raster Data for Earth Observation: An RDF Dataset of Territorial Unit Versions with their Land Cover

Ba-Huy Tran et al.

ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION (2020)

Article Computer Science, Information Systems

POI Mining for Land Use Classification: A Case Study

Renato Andrade et al.

ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION (2020)

Proceedings Paper Engineering, Electrical & Electronic

USING REMOTE SENSING IMAGES AND CLOUD SERVICES ON AWS TO IMPROVE LAND USE AND COVER MONITORING

K. R. Ferreira et al.

2020 IEEE LATIN AMERICAN GRSS & ISPRS REMOTE SENSING CONFERENCE (LAGIRS) (2020)

Review Engineering, Electrical & Electronic

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Meisam Amani et al.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2020)

Article Remote Sensing

Big spatial data for urban and environmental sustainability

Bo Huang et al.

GEO-SPATIAL INFORMATION SCIENCE (2020)

Article Geography, Physical

Meta-analysis of deep neural networks in remote sensing: A comparative study of mono-temporal classification to support vector machines

Shahriar S. Heydari et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2019)

Review Geography, Physical

Deep learning in remote sensing applications: A meta-analysis and review

Lei Ma et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2019)

Article Environmental Sciences

Understanding urban landuse from the above and ground perspectives: A deep learning, multimodal solution

Shivangi Srivastava et al.

REMOTE SENSING OF ENVIRONMENT (2019)

Article Computer Science, Interdisciplinary Applications

Collect Earth: An online tool for systematic reference data collection in land cover and use applications

David Saah et al.

ENVIRONMENTAL MODELLING & SOFTWARE (2019)

Article Engineering, Electrical & Electronic

EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification

Patrick Helber et al.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2019)

Article Geochemistry & Geophysics

Deep Learning for Hyperspectral Image Classification: An Overview

Shutao Li et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2019)

Article Environmental Sciences

Evaluation of Deep Learning CNN Model for Land Use Land Cover Classification and Crop Identification Using Hyperspectral Remote Sensing Images

Kavita Bhosle et al.

JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING (2019)

Article Engineering, Electrical & Electronic

Geospatial Big Data: New Paradigm of Remote Sensing Applications

Xingdong Deng et al.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2019)

Review Geography, Physical

Deep learning classifiers for hyperspectral imaging: A review

M. E. Paoletti et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2019)

Review Computer Science, Interdisciplinary Applications

Artificial neural networks and deep learning in urban geography: A systematic review and meta-analysis

George Grekousis

COMPUTERS ENVIRONMENT AND URBAN SYSTEMS (2019)

Proceedings Paper Geosciences, Multidisciplinary

A FAST AND PRECISE METHOD FOR LARGE-SCALE LAND-USE MAPPING BASED ON DEEP LEARNING

Xuan Yang et al.

2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019) (2019)

Review Computer Science, Interdisciplinary Applications

Soft Computing Techniques for Land Use and Land Cover Monitoring with Multispectral Remote Sensing Images: A Review

K. K. Thyagharajan et al.

ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING (2019)

Article Geochemistry & Geophysics

Recent Advances on Spectral-Spatial Hyperspectral Image Classification: An Overview and New Guidelines

Lin He et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2018)

Review Remote Sensing

Implementation of machine-learning classification in remote sensing: an applied review

Aaron E. Maxwell et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2018)

Review Remote Sensing

Land cover 2.0

Michael A. Wulder et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2018)

Article Environmental Sciences

Land cover and land use change analysis using multi-spatial resolution data and object-based image analysis

Sory I. Toure et al.

REMOTE SENSING OF ENVIRONMENT (2018)

Editorial Material Environmental Sciences

Remote Sensing Big Data: Theory, Methods and Applications

Peng Liu et al.

REMOTE SENSING (2018)

Article Computer Science, Information Systems

Using High-Performance Computing to Address the Challenge of Land Use/Land Cover Change Analysis on Spatial Big Data

Xiaochen Kang et al.

ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION (2018)

Article Environmental Sciences

Google Earth Engine Applications Since Inception: Usage, Trends, and Potential

Lalit Kumar et al.

REMOTE SENSING (2018)

Article Geography, Physical

Building block level urban land-use information retrieval based on Google Street View images

Xiaojiang Li et al.

GISCIENCE & REMOTE SENSING (2017)

Article Remote Sensing

Open land cover from OpenStreetMap and remote sensing

Michael Schultz et al.

INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION (2017)

Article Computer Science, Information Systems

Classifying urban land use by integrating remote sensing and social media data

Xiaoping Liu et al.

INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE (2017)

Article Remote Sensing

Subpixel land-cover classification for improved urban area estimates using Landsat

Andrew MacLachlan et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2017)

Article Remote Sensing

Hierarchical land cover and vegetation classification using multispectral data acquired from an unmanned aerial vehicle

Oumer S. Ahmed et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2017)

Article Geography, Physical

Multi-source remotely sensed data fusion for improving land cover classification

Bin Chen et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2017)

Article Environmental Sciences

Google Earth Engine: Planetary-scale geospatial analysis for everyone

Noel Gorelick et al.

REMOTE SENSING OF ENVIRONMENT (2017)

Review Environmental Sciences

Developments in Landsat Land Cover Classification Methods: A Review

Darius Phiri et al.

REMOTE SENSING (2017)

Review Computer Science, Information Systems

The Standardization and Harmonization of Land Cover Classification Systems towards Harmonized Datasets: A Review

Hui Yang et al.

ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION (2017)

Article Computer Science, Information Systems

Generating Up-to-Date and Detailed Land Use and Land Cover Maps Using OpenStreetMap and GlobeLand30

Cidalia Costa Fonte et al.

ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION (2017)

Article Multidisciplinary Sciences

A global dataset of crowdsourced land cover and land use reference data

Steffen Fritz et al.

SCIENTIFIC DATA (2017)

Article Geochemistry & Geophysics

Advances in Hyperspectral Image and Signal Processing A comprehensive overview of the state of the art

Pedram Ghamisi et al.

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE (2017)

Article Geochemistry & Geophysics

Deep Learning in Remote Sensing

Xiao Xiang Zhu et al.

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE (2017)

Review Geochemistry & Geophysics

Advanced Spectral Classifiers for Hyperspectral Images A review

Pedram Ghamisi et al.

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE (2017)

Article Computer Science, Interdisciplinary Applications

Utilizing Cloud Computing to address big geospatial data challenges

Chaowei Yang et al.

COMPUTERS ENVIRONMENT AND URBAN SYSTEMS (2017)

Article Computer Science, Interdisciplinary Applications

Parcel-based urban land use classification in megacity using airborne LiDAR, high resolution orthoimagery, and Google Street View

Weixing Zhang et al.

COMPUTERS ENVIRONMENT AND URBAN SYSTEMS (2017)

Article Computer Science, Information Systems

A land use/land cover change geospatial cyberinfrastructure to integrate big data and temporal topology

Jin Xing et al.

INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE (2016)

Article Environmental Sciences

Assessing Nebraska playa wetland inundation status during 1985-2015 using Landsat data and Google Earth Engine

Zhenghong Tang et al.

ENVIRONMENTAL MONITORING AND ASSESSMENT (2016)

Review Geography, Physical

Optical remotely sensed time series data for land cover classification: A review

Cristina Gomez et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2016)

Review Geography, Physical

Geospatial big data handling theory and methods: A review and research challenges

Songnian Li et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2016)

Article Engineering, Electrical & Electronic

Big Data for Remote Sensing: Challenges and Opportunities

Mingmin Chi et al.

PROCEEDINGS OF THE IEEE (2016)

Article Environmental Sciences

Mapping Urban Land Use by Using Landsat Images and Open Social Data

Tengyun Hu et al.

REMOTE SENSING (2016)

Article Green & Sustainable Science & Technology

Exploring Land Use and Land Cover of Geotagged Social-Sensing Images Using Naive Bayes Classifier

Asamaporn Sitthi et al.

SUSTAINABILITY (2016)

Article Geochemistry & Geophysics

Deep Learning for Remote Sensing Data A technical tutorial on the state of the art

Liangpei Zhang et al.

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE (2016)

Proceedings Paper Engineering, Electrical & Electronic

DEEP LEARNING APPROACH FOR LARGE SCALE LAND COVER MAPPING BASED ON REMOTE SENSING DATA FUSION

Nataliia Kussul et al.

2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) (2016)

Article Geography, Physical

Building a hybrid land cover map with crowdsourcing and geographically weighted regression

Linda See et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2015)

Review Environmental Sciences

Urban land cover classification using airborne LiDAR data: A review

Wai Yeung Yan et al.

REMOTE SENSING OF ENVIRONMENT (2015)

Article Plant Sciences

Assessing street-level urban greenery using Google Street View and a modified green view index

Xiaojiang Li et al.

URBAN FORESTRY & URBAN GREENING (2015)

Article Engineering, Electrical & Electronic

Urban Land Use and Land Cover Classification Using Remotely Sensed SAR Data through Deep Belief Networks

Qi Lv et al.

JOURNAL OF SENSORS (2015)

Article Computer Science, Artificial Intelligence

Geospatial Big Data: Challenges and Opportunities

Jae-Gil Lee et al.

BIG DATA RESEARCH (2015)

Article Environmental Studies

Free or low-cost geoinformatics for disaster management: Uses and availability issues

Richard M. Teeuw et al.

ENVIRONMENTAL HAZARDS-HUMAN AND POLICY DIMENSIONS (2013)

Article Remote Sensing

Land-cover classification of an intra-urban environment using high-resolution images and object-based image analysis

Carolina Moutinho Duque De Pinho et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2012)

Article Environmental Sciences

A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data

Zhixin Qi et al.

REMOTE SENSING OF ENVIRONMENT (2012)

Article Computer Science, Information Systems

Hybrid object-based approach for land use/land cover mapping using high spatial resolution imagery

Eva Savina Malinverni et al.

INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE (2011)

Article Remote Sensing

Landsat-comparable land cover maps using ASTER and SPOT images: a case study for large-area mapping programmes

Steven E. Franklin et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2011)

Article Remote Sensing

Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment

Robert Gilmore Pontius et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2011)

Article Environmental Sciences

Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data

Qingling Zhang et al.

REMOTE SENSING OF ENVIRONMENT (2011)

Article Environmental Sciences

Post-classification comparison of land cover using multitemporal Landsat and ASTER imagery: the case of KahramanmaraÅ, Turkey

Hakan Alphan et al.

ENVIRONMENTAL MONITORING AND ASSESSMENT (2009)

Article Environmental Sciences

Object-based land cover classification using airborne LiDAR

A. S. Antonarakis et al.

REMOTE SENSING OF ENVIRONMENT (2008)

Article Computer Science, Software Engineering

A visual tool for ontology alignment to enable geospatial interoperability

Isabel F. Cruz et al.

JOURNAL OF VISUAL LANGUAGES AND COMPUTING (2007)

Article Environmental Sciences

Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil

Rebecca L. Powell et al.

REMOTE SENSING OF ENVIRONMENT (2007)

Article Ecology

Effects of thematic resolution on landscape pattern analysis

Alexander Buyantuyev et al.

LANDSCAPE ECOLOGY (2007)