Earth Observation Analysis Using Random Forest Time-Series Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manual interpretation of remote sensing imagery is time-consuming, costly, and prone to human-induced errors due to factors like cloud cover and atmospheric distortion, and single-image observations become outdated quickly, leading to skewed results when analyzing temporal replication.
Innovation Solution
A computer-implemented process for Earth observation and analysis that includes preprocessing, machine learning, and Random Forest processing to filter and analyze satellite imagery, reducing noise and errors by generating per-pixel time-series filtering and descriptive statistics, and optimizing memory and processing speed through the use of 1D arrays and parallel computing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual interpretation of remote sensing imagery is used, then analysis can be performed with simple tools, but processing time and costs increase significantly
Solution Approach 1:
The patent replaces manual mechanical interpretation with automated machine learning systems. Random Forest algorithms and other ML models automatically process satellite imagery, eliminating the need for human analysts to manually examine and interpret images while dramatically increasing processing throughput and consistency.
Solution Approach 2:
The system enables self-service automated analysis where the machine learning models independently process imagery without human intervention. The algorithms automatically perform classification, feature extraction, and analysis tasks that previously required manual expert interpretation, making the process both faster and more scalable.
2Productivity
If single image observations are used, then processing is faster and simpler, but results become outdated quickly and accuracy decreases
Solution Approach 1:
The system performs preliminary actions by collecting and storing multiple satellite images over time before analysis. It aggregates temporal replication of raw satellite imagery in advance, building a time-series dataset that captures changes and patterns, thereby improving measurement precision while maintaining processing efficiency through automated batch processing.
Solution Approach 2:
The patent merges multiple single-image observations into a comprehensive time-series analysis. By combining information from multiple images taken at different times, the system creates a more accurate and comprehensive view of the observed phenomena, reducing errors from cloud cover, atmospheric distortion, and temporal variability.
3Measurement precision
If temporal replication of satellite imagery is aggregated, then measurement accuracy improves, but processing time increases
Solution Approach 1:
The patent replaces time-consuming manual processing of temporal data with automated machine learning systems. Random Forest algorithms and other ML models efficiently process aggregated time-series imagery, extracting features and generating statistics automatically, thereby maintaining high measurement precision while dramatically reducing processing time compared to manual methods.
Solution Approach 2:
The system changes parameters by transforming raw satellite imagery into derived products with specific temporal and spatial resolutions. It generates standardized time-series products with consistent parameterizations, enabling efficient processing while preserving the measurement precision benefits of temporal aggregation through optimized data structures and processing algorithms.
4Productivity
If machine learning and Random Forest processing are used, then processing speed and accuracy improve, but computational resource requirements increase
Solution Approach 1:
The patent segments the computational workload by dividing the processing into distinct stages: preprocessing of satellite imagery, feature extraction, Random Forest model training, and final analysis. This segmentation allows for optimized resource allocation at each stage, parallel processing where applicable, and efficient memory management, thereby improving processing speed while controlling computational resource consumption.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A process for Earth observation and analysis by pre-processing remote sensing images which may be from sources including MODIS, Proba-V, Landsat and/or Sentinel or any other space-borne or airborne sensor. Filtering the images by applying a temporal signal processing filter to a time series of remote sensing images and extracting descriptive statistics from image pixels of the remote sensing images to create input X parameters for use in a machine learning process. Applying the machine learning process to create a model which determines how the input X-parameter values map to the range of possible Y-parameter values in a way that improves RAM allocation and parallelizing in the software and processors during the machine learning process. Applying the output from machine learning to a potentially new Area of Interest to determine or predict Y-values for the known X-values using data scoring. Generating calibrated output images corresponding to the specific regions defining the Areas of Interest.