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Spatial Analysis, 3D Data & Machine Learning · LearnSpace
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Spatial Analysis, 3D Data & Machine Learning

Курс от Coursera
Начальный≈ 11.9 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Advance your skills in spatial analysis and machine learning for geospatial data. This course covers geostatistics, LiDAR and 3D data processing, and supervised machine learning techniques. You will also learn how to apply deep learning methods for imagery analysis. By the end of the course, you will be able to build and evaluate models for geospatial data and analyze complex spatial patterns.

Навыки, которые вы освоите

Feature EngineeringModel EvaluationSpatial Data AnalysisConvolutional Neural NetworksGeostatisticsMachine Learning MethodsRandom Forest AlgorithmFine-tuningModel TrainingSpatial AnalysisImage AnalysisDeep LearningGeospatial Information and TechnologyData AnalysisData ProcessingGeospatial MappingGeographic Information SystemsMachine LearningApplied Machine Learning

Программа курса

13 модулей · 72 учебных материалов

01Crunch Spatial Stats: Detecting Spatial Patterns with Global Moran’s I5 материалов
Why Location Changes the Story: Recognizing Spatial PatternsDIALOGUEGlobal Moran’s I Explained: Measuring Spatial AutocorrelationВидеоUnderstanding Global Moran’s I in Polygon-Based Spatial DataЧтениеHands-On Learning: Compute Global Moran’s I for Air-Quality Zones

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Professionals from the Industry

Преподаватель курса

Spatial Analysis, 3D Data & Machine Learning
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Обучение откроется на Coursera
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Обучение на Coursera

≈ 11.9 ч

13 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Немецкий, Испанский, Японский

Часть программы вашего университета
Задание
Practice Quiz: Interpreting Global Moran’s I ResultsЗадание
02Crunch Spatial Stats: Estimating Surfaces with IDW Interpolation6 материалов
Why Nearby Sensors Matter More: Thinking Spatially About DistanceDIALOGUEFrom Points to Surfaces: Why Interpolation MattersВидеоHow IDW Interpolation Works for Spatial PredictionВидеоWhen and Why to Use IDW InterpolationЧтениеHands-On Learning: Create an IDW Hotspot Map from Sensor DataЗаданиеPractice Quiz: Choosing and Evaluating IDW Interpolation OutputsЗадание
03Crunch Spatial Stats: Understanding Spatial Autocorrelation with Semivariograms6 материалов
Reading Distance Patterns: Making Sense of Spatial DependenceDIALOGUEWhy Distance Matters: Introducing the SemivariogramВидеоInterpreting Nugget, Sill, and Range in SemivariogramsВидеоHow Semivariograms Describe Spatial AutocorrelationЧтениеHands-On Learning: Interpret Semivariograms to Assess Spatial AutocorrelationЗаданиеGraded Assessment: Assessing Spatial Autocorrelation and Hotspot Mapping DecisionsЗадание
04Explore LiDAR in 3D: Visualize LiDAR Point Clouds in 3D5 материалов
Why 3D Matters for Elevation DataDIALOGUEHow LiDAR Represents the Real World in 3DЧтениеUnderstanding LiDAR Point CloudsВидеоStep-by-Step Walkthrough for loading LiDAR Point CloudsЧтениеHands-on Learning : Load and Explore a LiDAR Point CloudЗадание
05Explore LiDAR in 3D: Generate a DEM from Ground-Class Points 5 материалов
From Point Clouds to Terrain SurfacesЧтениеCreating a DEM from LiDAR PointsВидеоStep-by-Step Walkthrough for Generating DEMs from Ground Class pointsЧтениеHands-on Learning : Generate a DEM from Ground-Class PointsЗаданиеPractice Quiz : DEM Creation Readiness CheckЗадание
06Explore LiDAR in 3D: Assess Vertical Accuracy Using Control Points 6 материалов
From Numbers to DecisionsDIALOGUEWhat “Vertical Accuracy” Means for Flood ModellingЧтениеComparing DEM to Control PointsВидеоStep-by-Step Walkthrough for Assessing Vertical Accuracy of a DEMЧтениеHands-on Learning : Assess Vertical Accuracy of a DEMЗаданиеGraded Quiz : LiDAR Elevation Workflow TaskЗадание
07Train ML Models: From Pixels to Predictors6 материалов
Why Feature Engineering Matters in Land-Cover ClassificationDIALOGUEFrom Raw Imagery to Reliable ClassificationЧтениеExtracting Spectral and Texture Features Step-by-StepВидеоWalkthrough - Build a Feature Set for Land-Cover ClassesЧтениеHands-on Learning: Build a Feature Set for Land-Cover ClassesЗаданиеPractice Quiz: Feature Engineering CheckЗадание
08Train ML Models: Training a Random Forest Classifier on Imagery Data6 материалов
Why Random Forest Is a Strong Baseline for Land-Cover MappingDIALOGUERandom Forests for Beginners: The Big PictureВидеоRandom Forest as a Baseline for Land-Cover Classification ЧтениеWalkthrough - Train Your First Land-Cover ClassifierЧтениеHands-on Learning: Train Your First Land-Cover ClassifierЗаданиеPractice Quiz: Model Training CheckЗадание
09Train ML Models: Evaluating Accuracy: Confusion Matrices & Model Validation6 материалов
Why Accuracy Alone Can MisleadDIALOGUEUnderstanding the Confusion Matrix for Land-Cover DataВидеоEvaluating Land-Cover Classification Accuracy ЧтениеWalkthrough - Validate Your Land-Cover ModelЧтениеHands-on Learning: Validate Your Land-Cover ModelЗаданиеGraded Assessment: End-to-End Model EvaluationЗадание
10Deep Learn Imagery: Fine-Tuning CNNs for Land Cover6 материалов
Why Pre-Trained CNNs Are Essential for Remote SensingDIALOGUEFine-Tuning Pre-Trained CNNs and Feature Reuse for Satellite Imagery ВидеоAvoiding Spatial Data Leakage in Land-Cover Classification Models ЧтениеWalkthrough - Fine-Tune a CNN for Land Cover Classification ЧтениеHands-On Learning: Fine-Tune a CNN for Land Cover ClassificationЗаданиеPractice Quiz: CNN Fine-Tuning DecisionsЗадание
11Deep Learn Imagery: Improving Model Performance with Data Augmentation 6 материалов
Why Satellite Imagery Needs Data AugmentationDIALOGUEData Augmentation for Land-Cover ClassificationВидеоWhen Data Augmentation Hurts Model PerformanceЧтениеBuilding an Augmentation Pipeline for CNN TrainingВидеоWalkthrough - Implement and Evaluate a Data Augmentation PipelineЧтениеHands-On Learning: Implement and Evaluate a Data Augmentation PipelineЗадание
12Deep Learn Imagery: Explaining Model Predictions with Grad-CAM6 материалов
Why Model Explainability Builds TrustDIALOGUEUnderstanding and Using Grad-CAM for Land-Cover Classification ВидеоInterpreting and Communicating Grad-CAM Outputs in Remote Sensing ЧтениеWalkthrough – Which Model Would You Trust?ЧтениеHands-On Learning: Generate and Interpret Grad-CAM VisualizationsЗаданиеGraded Assessment: Deep Learn ImageryЗадание
13Project Module: Geospatial Machine Learning3 материалов
Why This Project MattersЧтениеProject RequirementsЧтениеGeospatial Machine LearningЗадание