A Systematic Evaluation of the COTQ Provincial Land Cover Product: Structural Consistency, Spectral Separability, and Relative Positioning Against ESA, ESRI, and Google Products
Summary
A systematic evaluation of the COTQ provincial land cover product for Quebec, comparing it with ESA WorldCover, ESRI LandCover, and Google DynamicWorld through structural consistency, spectral separability, and relative positioning analyses.
View Cached Full Text
Cached at: 09/17/26, 09:23 AM
# A Systematic Evaluation of the COTQ Provincial Land Cover Product: Structural Consistency, Spectral Separability, and Relative Positioning Against ESA, ESRI, and Google Products Source: [https://arxiv.org/abs/2609.17731](https://arxiv.org/abs/2609.17731) [View PDF](https://arxiv.org/pdf/2609.17731) > Abstract:High\-resolution land use and land cover \(LULC\) products derived from Sentinel\-2 imagery are widely used for environmental monitoring and land management, yet their performance can vary across regions with complex ecological gradients and heterogeneous surface conditions\. In Quebec, these limitations motivated the development of a provincial 10\-m land\-cover product, the COTQ, designed to support annual monitoring of land occupation and soil artificialisation\. This study presents a systematic evaluation of the COTQ product relative to three global 10\-m LULC datasets: ESA WorldCover, ESRI LandCover, and Google DynamicWorld\. This paper does not introduce a new mapping methodology but focuses on analysing the behaviour and consistency of the COTQ using complementary evaluation approaches\. All products are harmonized under a common legend and compared using structural indicators \(object\-size distributions, shape complexity, Adjusted Rand Index, and Intersection over Union\), spectral separability metrics derived from Sentinel\-2 reflectance data, and a targeted photo\-interpretation of disagreement areas\. The analysis is conducted over eight Sentinel\-2 tiles selected to represent the main bioclimatic domains of Québec, from temperate and boreal forests to northern tundra environments\. The results show that the COTQ exhibits structural and spectral characteristics most similar to ESA WorldCover among the reference global products, while revealing systematic differences linked to class definitions and thematic priorities, particularly for urban areas, wetlands, and rocky or cryptogamic surfaces\. This multi\-criteria evaluation provides an objective characterization of the COTQ product and clarifies its relative positioning with respect to existing global land\-cover datasets for operational land monitoring in Québec\. ## Submission history From: Samuel Foucher \[[view email](https://arxiv.org/show-email/ec1b46d3/2609.17731)\] **\[v1\]**Tue, 15 Sep 2026 18:41:15 UTC \(2,582 KB\)
Similar Articles
GeoNatureAgent Benchmark: Benchmarking LLM Agents for Environmental Geospatial Analysis Across Frontier and Open-Weight Foundation Models
The paper introduces GeoNatureAgent Benchmark, the first benchmark for evaluating LLM agents on environmental geospatial analysis tasks via structured tool calls. It evaluates seven models on 93 tasks across 18 categories and finds Claude Sonnet 4 achieves highest accuracy at 60.8%, while open-weight models like DeepSeek V3.2 offer strong cost-performance tradeoffs.
Scalable and Trustworthy Earth Observation Foundation Models
This chapter reviews design principles and current landscape of foundation models for Earth observation, highlighting the need for domain-specific adaptation, physically plausible representations, and consistent evaluation benchmarks. It includes case studies on harmful algal bloom prediction and adaptive monitoring station selection.
Benchmarking Composed Image Retrieval for Applied Earth Observation
This paper presents a unified benchmark for composed image retrieval in Earth observation, evaluating vision-language backbones and introducing a change-centric dataset (xView2-CIR) for disaster monitoring, highlighting distinct challenges compared to attribute-based retrieval.
Now We Know? A Systematic Comparison of TerraMind and THOR
This paper presents a systematic comparison of two geospatial foundation models, TerraMind and THOR, developed under ESA's φ-lab, analyzing how architectural choices like patch size and decoder type affect performance across ten use cases in Earth observation tasks.
SpatialCLI: Learning to Reason With Spatial Tools, Then Without Them
SpatialCLI presents a framework that trains vision-language models to use specialist spatial tools and then internalize those capabilities, boosting Qwen3-VL-8B-Instruct on the MindCube benchmark from 29.3% to 84.6% with tools.