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    Spatio-Temporal LULC Mapping

    LULC mapping integrates satellite remote sensing, cloud platforms like Google Earth Engine (GEE), and machine learning to monitor land cover changes over time, enabling detection of urban expansion, agricultural shifts, and ecosystem impacts at scale.

    Evidence Overview

    • Satellite remote sensing provides efficient acquisition of large-scale land use/land cover change data for accurate mapping products.
    • Advancements in remote sensing, cloud computing via GEE, and machine learning enhance analysis of urban growth and LULC dynamics across temporal scales.
    • Classification performance relies on producer's and user's accuracy metrics.
    • Human land use influences land cover, driving ecosystem distribution and services.
    • Sustainable land management practices to reduce emissions can generate competing land demands.

    Analysis

    Workflow

    INPUT(satellite data, cloud resources, temporal stack, labels) [Load Inputs]

    SET(processing_platform, GEE) [Select Platform] for large-scale multi-date data.

    SET(sensor_inputs, Landsat/Sentinel) [Choose Sensors] for repeated observations.

    • FOR(each date in stack) [Process Temporal Loop]

    SET(classifier, ML model) [Assign Classifier] for LULC and change detection.

    • IF(semantic enhancement needed) [Check Accuracy Boost]
    • IF(forecast needed) [Optional Prediction]
    • APPLY(ANN/cellular automata) [Project Trajectories].

    RETURN(maps, changes, metrics) [Deliver Results]

    Core Methods and Considerations

    Workflow: Acquire multi-temporal imagery (Landsat, Sentinel) via GEE; preprocess for clouds and consistency; classify with ML (Random Forest, SVM, CNNs); detect changes; validate accuracies; optionally forecast with ANNs.

    Enhancements: Semantic boosting refines deep learning in complex areas.

    Resolutions: Use 30m for regional, 10m for detailed studies; annual for slow changes, monthly for dynamic ones.

    Challenges: Training data quality, mixed pixels, spectral confusion; address via fusion and ground truth.

    Uncertainties

    Specific producer's/user's accuracy values unavailable; prediction models optional and trend-dependent; class separability varies by landscape; competing land demands from sustainability efforts unquantified.

    sciencedirect.com faviconsciencedirect.com

    Land Cover Mapping with Satellites

    This article reviews methods for creating land use and land cover (LULC) maps using data from satellite remote sensing. It highlights the benefits of using satellite imagery for large-scale LULC analysis.
    Satellite remote sensing provides efficient acquisition of large-scale land use/land cover change data for accurate mapping products.
    nature.com faviconnature.com

    Urban Growth Analysis

    This study uses the Google Earth Engine platform and predictive models to analyze how cities grow and land use changes over time. It demonstrates the power of cloud computing and machine learning for monitoring urban development.
    Advancements in remote sensing, cloud computing via GEE, and machine learning enhance analysis of urban growth and LULC dynamics across temporal scales.
    eprints.whiterose.ac.uk faviconeprints.whiterose.ac.uk
    🇬🇧

    Deep Learning for LULC

    This research explores a method called semantic boosting to improve the accuracy of deep learning models used for classifying land use and land cover. It focuses on enhancing the performance of these models for more precise LULC mapping.
    Classification performance relies on producer's and user's accuracy metrics.
    biodiversity.europa.eu faviconbiodiversity.europa.eu
    🇪🇺

    Land Use Change & Biodiversity

    Land use by humans significantly impacts ecosystems and the services they provide, influencing the distribution and function of biodiversity across Europe.
    Human land use influences land cover, driving ecosystem distribution and services.
    heigit.org faviconheigit.org

    Land Use & Climate

    Sustainable land management practices are crucial for reducing emissions, but can also create competing demands for land resources, as discussed in the HeiGIT Climate Action Navigator.
    Sustainable land management practices to reduce emissions can generate competing land demands.