With the continuous development of multiple remote sensing platforms such as aviation, aerospace, and near-Earth space, remote sensing technology has advanced by leaps and bounds in recent years. As a result, the spatial, temporal, and spectral resolution of remote sensing data has been continuously improved, and the amount of data has also increased significantly, making it more and more characteristic of big data. For related research, the emergence of remote sensing big data provides unprecedented opportunities, but it also poses huge challenges. Traditional workstations and servers have been unable to meet the needs of large-scale, multi-scale massive remote sensing data processing.
In order to solve this problem, many global-scale earth science data (especially satellite remote sensing data) online visualization computing and analysis cloud platforms such as Google Earth Engine (GEE) and Aerospace Hongtu's PIE Engine have emerged at home and abroad. Among them, Earth Engine is the most powerful, capable of accessing and synchronizing satellite images such as MODIS, Landsat, and Sentinel, and meteorological reanalysis data sets such as NCEP, which are currently commonly used in the field of remote sensing. These data are processed. At present, Earth Engine contains more than 900 public datasets, and about 2 PB of data are added every month, with a total capacity of more than 80 PB. As the most advanced remote sensing cloud platform in China, PIE Engine has developed very rapidly in recent years. It has abundant domestic satellite data and other important open source data in China, and has unique advantages in data security and access convenience. Compared with traditional image processing tools (such as ENVI), the remote sensing cloud platform provides abundant computing resources on the one hand; on the other hand, its huge cloud storage capacity saves a lot of data download and preprocessing time for researchers.
Nowadays, remote sensing cloud platforms such as GEE/PIE are attracting the attention of more and more domestic and foreign scientific and technological workers with their powerful functions, and the scope of application is also expanding. Committed to helping researchers master the practical application capabilities of GEE and PIE, based on the JavaScript programming language, combined with examples to explain the basic concept knowledge of remote sensing cloud, image big data analysis, classic application cases and other advanced skills. In order to improve the teaching quality, the most advanced AI natural language model such as ChatGPT will be integrated to assist teaching, assist students to answer doubts, provide targeted suggestions and guidance, not only allow students to grasp the course content more deeply, but also provide efficient personality for future self-learning personalized learning experience.
Topic 1 Introduction to GEE and PIE Remote Sensing Cloud Platform
1. Introduction to GEE and PIE platforms and typical application cases
2. JavaScript basics, including variables, operators, arrays, judgments and loop statements, etc.
3. Important concepts of remote sensing cloud and typical data analysis process
4. Basic objects and platforms of remote sensing cloud
4.1 Image and Image Set
4.2 Geometry, Elements, and Feature Set
4.3 Date, Character, Number
4.4 Array, List, Dictionary
4.5 Image/Image Set, Feature/Element Set Data Query, Spatiotemporal Filtering, Visualization, Attribute Viewing and other Main Objects Most Commonly Used API Introduction
Topic 2 Basics of GEE and PIE Image Big Data Processing
1. 1. Explanation of key knowledge points
1.1.1 Mathematical operations on images, relations/conditions/Boolean operations, morphological filtering, texture feature extraction, etc. 1.1.2
Image masking, cropping and mosaicing
1.1.3 Cyclic iteration of collection objects (map/ iterate)
1.1.4 Collection object joint (Join)
1.1.5 Image object-oriented analysis
2. 2. Main function lectures and drills
2.2.1 Landsat/Sentinel-2 image batch cloud removal
2.2.2 Landsat/Sentinel-2 sensor normalization, vegetation index calculation, etc.
2.2.3 Smoothing and spatial interpolation of time series optical images
Topic 3 Data Integration Reduce
1. Explanation of key knowledge points
1.1 Integration of images and image sets, such as annual image synthesis of specified time windows
1.2 Image area statistics and domain statistics, post-classification processing
1.3 Feature set attribute column statistics
1.4 Mutual conversion between raster and vector
1.5 Group integration and Zonal Statistics
1.6 Linear Regression Analysis of Image Sets, Image and Feature Sets
2. Main function lectures and drills
2.1 Statistical analysis of the number of available Landsat images and the number of cloud-free observations in the study area
2.2 Synthesis of annual NDVI vegetation numbers in China and the search for the greenest DOY time of the year
2.3 Analysis of the temporal and spatial variation trend of rainfall at the national scale and 30-year scale
Topic 4 Cloud Data Visualization
1. Explanation of key knowledge points
1.1 Element and feature set attribute mapping (bar graph, histogram, stacked column graph, scatter plot, etc.)
1.2 Image mapping (area statistics, classification map, histogram, scatter plot, line type) Graphs, pie charts, etc.)
1.3 Image set mapping (sample point time series graph, regional statistical time series graph, etc.)
1.4 Array and linked list graphing (scatter plot, line transect graph, etc.)
1.5 Graphic style and property settings
2. Lectures and drills on main functions
2.1 Analysis and mapping of different surface vegetation phenology based on MODIS time series images
2.2 Analysis of annual forest spatiotemporal changes and thematic map drawing based on Hansen products
Topic 5 Data import and export and asset management
1. Explanation of key knowledge points
1.1 Different vector data upload personal assets
1.2 Image data upload personal assets, attribute settings, etc.
1.3 Image batch export (Asset and Driver)
1.4 Vector data export (Asset and Driver)
1.5 Spatial statistical analysis result export
2. Main function lectures and drills
2.1 PIE platform domestic satellite data download
2.2 Batch export and download of image synthesis
2.3 Export of remote sensing index data corresponding to ground samples
Topic 6 Machine Learning Algorithms
1. Explanation of key knowledge points
1.1 Sample sampling (random sampling, stratified random sampling)
1.2 Supervised classification algorithms (random forest, CART, Bayesian, SVM, decision tree, etc.)
1.3 Unsupervised classification algorithms (wekaKMeans, wekaLVQ, etc.)
1.4 Classification Accuracy Evaluation
2. Lectures and drills on main functions
2.1 Forest dynamic monitoring based on combined optical and radar time-series images
2.2 Automatic water extraction and flood monitoring
Topic 7 Special Topic Practice and Review
1. A comprehensive case of GEE land use classification, realizing the main functions of the series, including ground sample preparation, multi-source remote sensing image preprocessing, algorithm development, classification postprocessing, accuracy assessment, spatial statistical analysis and mapping, etc.
2. Explanation and learning of classic P IE case codes 2.1 Night light index extraction 2.2 Long-term scale vegetation coverage inversion 2.3 Water dynamic monitoring 2.4 Crop planting area extraction 2.5 Desertification degree extraction
3. Analysis of dynamic changes in population density, student recruitment case explanation and Q&A
3.1 GEE and PIE platform switching, code optimization, common errors and debugging summary
