EconologyResearch Foundation - Technical Performance Report

Economic X-Ray Systems & Data Diagnostics

Universal temporal, spatial, and spectral analytical data engines tracking 200 operational features simultaneously.
Spatial disjoint block matrix sorting protocols enforcing a rigid 70/30 data split to eliminate spatial autocorrelation leakage.
Out-of-sample projection verification testing models across 16 high-dimensional agricultural and structural target classes.

High-Dimensional Parallel Performance Matrix Summary

The statistical comparative analysis below documents the cross-sectional classification accuracies achieved by the MIP-015 Linear Subspace Transformation Matrix against the MIP-014 Multi-Modular Non-Linear Parallel Compression Engine.

Class ID	Ground-Truth Class Name	Total Labeled Pixels	MIP-015 PCA Accuracy (%)	MIP-014 Non-Linear Accuracy (%)
1	Alfalfa	46	82.6	91.3
2	Corn-notill	1428	84.2	93.8
3	Corn-mintill	830	81.5	92.1
4	Corn	237	89.0	94.5
5	Grass-pasture	483	91.3	95.2
6	Grass-trees	747	94.8	98.1
7	Grass-pasture-mowed	28	78.5	85.7
8	Hay-windrowed	478	97.9	99.4
9	Oats	20	75.0	80.0
10	Soybean-notill	972	80.4	91.6
11	Soybean-mintill	2455	86.1	94.3
12	Soybean-clean	593	83.7	92.9
13	Wheat	205	96.5	98.5
14	Woods	1265	95.1	98.9
15	Buildings-Grass-Trees-Drives	386	88.3	94.8
16	Stone-Steel-Towers	93	92.4	96.7

Subspace Variance Analysis

Figure 1: MIP-015 Orthogonal Subspace Variance Distribution Matrix