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Related Concept Videos

Levels of Use of a GIS01:29

Levels of Use of a GIS

Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

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Methods of Obtaining Topography

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Using GeoAI and machine learning tools for consistent high-resolution land cover mapping based on time-series NAIP

Jie Liu1,2, Xusheng Tang3, Chao Wang1,4

  • 1Department of Geography and Environment, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA.

Landscape Ecology
|June 11, 2026
PubMed
Summary
This summary is machine-generated.

This study developed a GeoAI/ML algorithm to create consistent 1-m land-cover maps from National Agricultural Imagery Program (NAIP) data. The method successfully addresses sensor variations, enabling reliable multi-decade land-cover analysis.

Keywords:
Deep learningGeoAIHigh resolution land-cover mapsLand cover classificationMachine learningNAIP

Related Experiment Videos

Area of Science:

  • Environmental Science
  • Remote Sensing
  • Geospatial Artificial Intelligence (GeoAI)

Background:

  • High-resolution land-cover maps are crucial for ecological and landscape analysis.
  • Scarcity of long-term, high-resolution time-series land-cover products hinders research.
  • Temporal heterogeneity in National Agricultural Imagery Program (NAIP) data, due to sensor shifts, complicates consistent multi-year mapping.

Purpose of the Study:

  • To develop a GeoAI/Machine Learning (ML) algorithm for producing spatially detailed and temporally coherent 1-m land-cover maps using time-series NAIP imagery.
  • To overcome challenges posed by cross-year sensor characteristic shifts in NAIP data without requiring historical training samples.

Main Methods:

  • A dual-track adaptive workflow was designed to handle NAIP imagery of varying quality.
  • High-quality NAIP imagery (2009-2017) was classified using a foundation model (U-Net/ResNet-34) refined by a Segment Anything Model (SAM).
  • Lower-quality imagery (2004-2008) was reconstructed via an NLCD-based spatiotemporal bridging and label back-casting pipeline.

Main Results:

  • The algorithm achieved stable, consistent results across years with high overall accuracies (e.g., 0.887 in 2014, 0.733 in 2004).
  • High F1-scores were consistently observed for Structure, Water, Wetland, and Cropland classes.
  • The algorithm maintained spatial fidelity and temporal consistency despite substantial sensor differences in historical imagery.

Conclusions:

  • GeoAI/ML tools and diverse data sources can effectively generate consistent, high-resolution, multi-decade land-cover maps from NAIP imagery.
  • The developed approach offers a scalable solution for generating time-series land-cover maps across the USA.
  • This supports enhanced long-term land-change analyses and landscape-level planning.