GIS Mapping Software

🌏 🛰️ Supervised Land Use Classification Using Remote Sensing and Machine Learning 🛰️ 🌏 Land use classification is critical in understanding how human activities shape the environment and guiding sustainable development. In this learning experience, I explored how satellite imagery, field data, and machine learning techniques can be combined to generate detailed land use maps for effective spatial analysis and decision-making. 📊 Methodology ✔️ Field Data Collection- Collected 60 ground truth points across five land use classes (Built-up, Vegetation, Paddy, Bare Land, and Water Bodies) using QField linked with QGIS. ✔️ Satellite Data Processing- Processed Landsat 8 imagery in Google Earth Engine (GEE) to extract spectral signatures for each land use class. ✔️ Classification Model- Applied the Support Vector Machine (SVM) algorithm in Google Colab for supervised classification. ✔️ Accuracy Assessment- Validated the classification using a confusion matrix, user/producer accuracy metrics, and the kappa coefficient from both manually and Google Colab. 📈 Results ▪️ The model achieved an overall classification accuracy of 65% with moderate agreement (Kappa = 0.55). ▪️ Built-up area showed higher classification accuracy (70.59 % producer accuracy, 66.67% user accuracy), while Bare Land and Paddy classes had relatively lower accuracy, highlighting areas for future improvement. 🔎 Why This Matters This study demonstrates how integrating ground truth data, satellite imagery, and open-source tools enables practical, cost-effective land use classification, especially valuable for resource and data-limited areas. These insights can guide urban planning, agriculture management, environmental protection, and disaster risk planning. By applying machine learning in geospatial analysis, this study helps connect data to real-world solutions, making it a valuable approach for building more sustainable and resilient communities. #GIS #RemoteSensing #LandUseClassification #MachineLearning #GoogleEarthEngine #QField #QGIS #SpatialData #GeospatialAnalysis #UrbanPlanning #SustainableDevelopment #DataDrivenPlanning

Following the publication of my paper here https://lnkd.in/eP4AfWty, I’m ready to share my Random Forest-based machine learning scripts for Land Use Land Cover (LULC) classification in Gaborone, Botswana. The model was enhanced using the following indices: NDVI (Vegetation), NDBI (Built-up Index), NDWI (Water Index), BSI (Bare Soil Index). These indices improve class separability, making the classification process more accurate. The scripts, especially for the 2005 classification, also include a scan line error correction function for Landsat 7 images and a pan-sharpening function to enhance radiometric resolution by merging high-resolution panchromatic data with multispectral bands. Access the scripts here: 🔗 https://lnkd.in/eYbQs3Dy #GIS #remotesensing #machinelearning #lulc #geospatialanalysis #gee

🌍 Predicting Future Land Use and Land Cover (LULC) with - MOLUSCE plugin I’ve just published a new YouTube tutorial where I demonstrate how to simulate and validate future Land Use & Land Cover using the MOLUSCE plugin in QGIS. In this video, I walk through the complete workflow: ✅ Data preparation (LULC, DEM, distance from rivers & roads, slope map) ✅ Correlation evaluation (Pearson’s, Cramér’s V, Joint Information Uncertainty) ✅ Area change analysis ✅ Transition potential modeling (Logistic Regression, Multi-layer Perceptron, Weights of Evidence) ✅ Running the simulation ✅ Validation 🔗 Watch the full tutorial on predicting future LULC here: 👉 https://lnkd.in/gR7UykpS 🔗 Link to my YouTube channel: 👉 https://lnkd.in/gttuvzD2 📂 Tutorials to download data from different sources 🔗 How to download DEM from USGS: 👉 https://lnkd.in/gzgbgYZW 🔗 How to download river, road, LULC, and DEM from Diva GIS 👉 https://lnkd.in/gqm5fAWG 🔗 How to download Landsat LULC from USGS: 👉 https://lnkd.in/g6dQ42QW 🔗 LULC Unsupervised classification: 👉 https://lnkd.in/gpyUq7gu 🔗 Use a shapefile to download any topography map from USGS: 👉 https://lnkd.in/gm5HQ2Sg ✨ Whether you’re a student, researcher, or GIS professional, this tutorial will help you understand how to predict and validate land cover. 📢 If you find this useful, please share or repost — it might help someone in your network! 🌟 Let’s learn, grow, and model better together! #QGIS #GIS #RemoteSensing #LULC #Geospatial #MOLUSCE #QGISTutorial #SpatialAnalysis #MachineLearning #WatershedManagement #ArcGIS #Hydrology #WaterResources #FreeCourse #BeramaAcademy #ClimateChange #HydrologicalModeling #Watershed #ANN

🚀 Land Cover Classification using SVM & LISS-IV(5m) Data in R 🌍📊 🔍 How can we achieve high-precision land cover classification using machine learning and remote sensing? In this project, I used LISS-IV satellite imagery and Support Vector Machine (SVM) Classification in R to generate an accurate land cover map. The workflow included: ✅ DN to TOA reflectance conversion for accurate spectral analysis ✅ Feature extraction – NIR, Red, Green bands & spectral indices like NDVI, GNDVI, NDWI ✅ Training data preparation using labeled shapefiles ✅ SVM-based classification with 10 epochs and 5-fold cross-validation (cv_folds = 5) for optimal accuracy ✅ Accuracy assessment – achieving 91.23% overall accuracy 🎯 📍 Why SVM? Support Vector Machine (SVM) is a powerful supervised learning algorithm that excels in high-dimensional space, effectively separating classes using an optimal decision boundary and performing well even with limited training data. 📌 Data Source: LISS-IV imagery 📌 Implementation: R programming with the following libraries: 🔹 terra – for raster data processing 🔹 sf – for handling vector data 🔹 lubridate – for date-time management 🔹 dplyr – for data manipulation 🔹 readr – for reading and writing CSV files 🔹 e1071 – for SVM classification 🔹 caret – for model training and validation 🔹 ggplot2 – for visualization 🔹 RColorBrewer – for color styling 🔗 Want to explore more? 📂 GitHub Repo: [You can try it here! 👉 https://lnkd.in/djSBDws7] 🌍 Dataset Source: Bhoonidhi (ISRO) [https://lnkd.in/dcb6JuP8] For more insights, feel free to connect with me: 🔹 Google Scholar: [https://lnkd.in/dZUUJN-4] 🔹 ResearchGate: [https://lnkd.in/dzPz52m3] 🔹 GitHub: [https://github.com/Roysubh] 🔹 ORCiD: [https://lnkd.in/gRXKaz74] #RemoteSensing #GIS #MachineLearning #LandCoverClassification #RStats #SVM #SatelliteImagery #GeospatialAnalysis #DataScience #LISS_IV #ISRO #SpectralIndices #NDVI #EarthObservation #SpatialAnalysis #RasterData #OpenSource #LC #ImageClassification #EnvironmentalMonitoring #RLanguage #GeospatialTech #RemoteImagery #ClassificationAlgorithm #SpatialModeling #TOAReflectance #TerraR #CrossValidation #AccuracyAssessment #Bhoonidhi #IndianRemoteSensing #GeoAI #Geoinformatics🚀

🚨 #New #Publication #Alert! 📡🌍🧠 Thrilled to share our latest research article just published in Ecological Engineering & Environmental Technology: "#Predicting #Land #Use Land Cover Dynamics Using #Machine #Learning and Satellite Imagery: A Case Study from the Sebou Basin, Morocco" 🔗 https://lnkd.in/evu3c2Cr In this work, we explored over 30 years of #LULC changes in the Oued Ouergha watershed using multi-temporal #Landsat imagery and powerful machine learning algorithms — #Gradient #Boosting (GB), Random Forest (#RF), and Support Vector Machines (#SVM) — to detect patterns and predict land cover in 2033. 🚀 What’s innovative about our approach? Use of #CA-#ANN modeling via QGIS MOLUSCE for LULC prediction Comparison of three robust ML classifiers for classification accuracy Integration of spatial drivers like elevation and proximity to roads in LULC forecasting Identification of alarming trends: loss of agricultural and forest areas, increase in bare soil and urban sprawl 🧠 Our Gradient Boosting model achieved up to 99.8% accuracy, showing superior performance for mapping and predicting land transformations. This study is a step forward in how machine learning and open satellite data can help design data-driven policies for sustainable land management, particularly in semi-arid regions under environmental pressure. 📍 Case Study: Sebou Basin, Morocco 🧑🔬 Authors: Brahim Meskour, Mohammed Hssaisoune, Adnane Labbaci, #Zouhir #Dichane, Mohamed Aghenda, Yohann Cousquer, Lhoussaine Bouchaou 🙏 Acknowledgments to #CNRST, the OCP Foundation, and #IRD for their support and funding through the GEANTech program. #LULC #RemoteSensing #MachineLearning #LandDegradation #Morocco #GeoAI #QGIS #DigitalTwins #SustainableDevelopment #GoogleEarthEngine #EcologicalEngineering #GIS #SatelliteImagery #SebouBasin #ResearchPublication

A Methodology For The Multitemporal Analysis Of Land Cover Changes And Urban Expansion Using Synthetic Aperture Radar (SAR) Imagery - A Case Study Of The Aburrá Valley In Colombia -- https://lnkd.in/gP4ZhhN9 <-- shared paper -- “The Aburrá Valley, NW region of Colombia, has undergone significant land cover changes and urban expansion in recent decades, driven by rapid population growth and infrastructure development. This region, known for its steep topography & dense urbanization, faces considerable environmental challenges. Monitoring these transformations is essential for informed territorial planning & sustainable development. This study leverages Synthetic Aperture Radar (SAR) imagery from the Sentinel-1 mission, covering 2017–2024, to propose a methodology for the multitemporal analysis of land cover dynamics and urban expansion in the valley. The novel proposed methodology comprises several steps: first, monthly SAR images were acquired for every year under study from 2017 to 2024, ensuring the capture of surface changes. These images were properly calibrated, rescaled, and co-registered. Then, various multitemporal fusions using statistics operations were proposed to detect and find different phenomena related to land cover and urban expansion. The methodology also involved statistical fusion techniques—median, mean, and standard deviation—to capture urbanization dynamics... The results highlight a marked increase in urbanization, particularly along the valley’s periphery, where previously vegetated areas have been replaced by built environments. Additionally, the visual inspection analysis revealed areas of high variability near river courses and industrial zones, indicating ongoing infrastructure and construction projects. These findings emphasize the rapid and often unplanned nature of urban growth in the region, posing challenges to both natural resource management & environmental conservation efforts. The study underscores the need for the continuous monitoring of land cover changes using advanced remote sensing techniques like SAR, which can overcome the limitations posed by cloud cover and rugged terrain. The conclusions drawn suggest that SAR-based multitemporal analysis is a robust tool for detecting & understanding urbanization’s spatial and temporal dynamics in regions like the Aburrá Valley, providing vital data for policymakers and planners to promote sustainable urban development and mitigate environmental degradation…” #GIS #spatial #mapping #SyntheticApertureRadar #SAR #remotesensing #multitemporalanalysis #landcover #landcoverchange #clustering #kurtosis #fuzzylogic #kernelbasedmethod #machinelearning #spatialanalysis #spatiotemporal #geostatistics #model #modeling #AburráValley #Columbia #urban #urbanexpansion #population #growth #topography #monitoring #satellite #sentinel #valley #landuse #distribution #infrastructure #building #roads #naturalresources #environmental #conservation #monitoring #multitemporal 

ML for satellite imagery goes beyond single-scene classification. Two methods dominate production pipelines: 🌍 Time series classification > temporal stacks capture seasonal and phenological cycles, improving land use and land cover mapping. 🔎 Multi-scale change detection > combining local detail with regional context reduces false positives in urban expansion, deforestation, or post-disaster monitoring. Neither works without high-quality annotations (!) Labels define class boundaries in time series and provide reference points for multiscale detection. Validation loops are equally important – misaligned training and reference data can bias results fast. These steps show why satellite imagery analysis requires an end-to-end ML workflow, not isolated models. Check the full pipeline explanation in the comments 💡 #SatelliteImagery #RemoteSensing #GeospatialML #MachineLearning #DataAnnotation