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Advanced Land Cover Classification for Carbon Baselines and NbS Feasibility

Kumi Analytics delivers consistent, standards-aligned land cover mapping from national wall-to-wall products (10 m and 30 m resolution) to project-level feasibility assessments. Our land cover products are specifically designed to support Nature-Based Solutions project development — providing the deforestation baselines, forest/non-forest classifications, and ecosystem type maps required by Verra, Gold Standard, and REDD+ national accounting frameworks. The same taxonomy applies at every scale, from country-wide screening to individual project sites.

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Landcover classification and national mapping

Our Landcover Capabilities

National & Regional Mapping

Wall-to-wall classification at 10 m or 30 m resolution with a consistent legend for countries or regions—ideal for reporting, baselines, and policy.

Multi-Scale Classification

Same taxonomy from national down to project scale. Compare country-level trends with site-level feasibility without reconciling different legends.

Change Over Time

Landcover change layers and time series for deforestation, urban expansion, agriculture, and restoration—with clear timelines for reporting.

Custom Legends

Align to national standards (e.g. CORINE, ministry schemes) or project-specific classes. We adapt the legend so outputs fit your workflows.

Cross-Sector Applications

One product for carbon, agriculture, urban planning, biodiversity, water, and ESG. Single source of truth across teams and reporting.

Feasibility & Screening

Fast, coarse classification to shortlist areas for projects—from national screening to initial site feasibility before detailed surveys.

Flexible Taxonomy, Every Scale

Landcover classification at multiple scales

Consistent Classification Across Resolutions

We use a single, coherent landcover taxonomy so that national maps and project-level maps speak the same language. That means:

  • One legend from country-wide to site-level—no reconciling different systems.
  • Standards-aligned options (e.g. UN-FAO LULC–style) for reporting and audits.
  • Same classes support carbon, agriculture, planning, and biodiversity use cases.

Multi-Source Fusion

Optical and SAR data are combined to improve robustness to cloud cover and seasonal gaps. You get stable classification across seasons and regions.

Validation & Uncertainty

Accuracy assessment and, where needed, uncertainty quantification so outputs are defensible for reporting, audits, and decision-making.

Benefits for Your Organisation

Single Source of Truth

One landcover product for carbon, agriculture, planning, and reporting—reducing duplication and inconsistency across teams.

Faster Feasibility

Move from national or regional screening to project shortlists quickly, with the same taxonomy at every scale.

Standards-Aligned

Outputs compatible with UN-FAO LULC, IPCC, and national reporting frameworks—so they slot into existing processes.

From National Mapping to Project-Level Feasibility

Tell us your geography and use case—we can support wall-to-wall mapping, custom legends, or rapid feasibility screening.

Discuss Your Use Case

Frequently asked questions

We offer standards-aligned options (e.g. UN-FAO LULC–style) and can tailor legends to national systems (e.g. CORINE) or project-specific classes. The same taxonomy can be applied at different resolutions so national and project-level products stay consistent.
Yes. We use one coherent taxonomy across scales—from country-wide wall-to-wall mapping to higher-resolution project screening. You can compare national baselines with site-level feasibility without reconciling different legends.
Our landcover products support agriculture, urban and spatial planning, biodiversity and habitat assessment, water and catchment analysis, disaster risk, and ESG reporting. Including carbon projects: the same maps can feed into carbon baselines and project screening.
We combine optical and SAR imagery where appropriate to improve coverage in cloudy regions and across seasons. Composites and multi-temporal analysis help deliver stable classification even with gaps in single-date imagery.