GPRNet: A Geometric Prior-Refined Semantic Segmentation Network for Land Use and Land Cover Mapping

Abstract

What are the main findings?

We propose GPRNet, a geometry-aware semantic segmentation framework that integrates a Geometric Prior-Refined Block (GPRB) and a Mutual Calibrated Fusion Module (MCFM) to enhance boundary sensitivity and cross-stage semantic consistency.

GPRB leverages learnable directional derivatives to construct structure-aware strength and orientation maps, enabling more accurate spatial localization in complex scenes.

MCFM introduces geometric alignment and semantic enhancement mechanisms that effectively reduce the encoder–decoder feature gap.

GPRNet achieves consistent performance gains on ISPRS Potsdam and LoveDA, improving mIoU by up to 1.7% and 1.3% respectively over strong CNN-, attention-, and transformer-based baselines.

What are the implications of the main findings?

Incorporating geometric priors through learnable gradient-based features improves the model’s ability to capture structural patterns and preserve fine boundaries in high-resolution remote sensing imagery.

The mutual calibration mechanism demonstrates an effective design for encoder–decoder interaction, showing potential for broader applicability across segmentation architectures and modalities.

The empirical evidence indicates that geometry-informed representation learning can serve as a general principle for enhancing land-cover mapping in diverse and structurally complex environments.

Semantic segmentation of high-resolution remote sensing images remains a challenging task due to the intricate spatial structures, scale variability, and semantic ambiguity among ground objects. Moreover, the reliable delineation of fine-grained boundaries continues to impose difficulties on existing CNN- and transformer-based models, particularly in heterogeneous urban and rural environments. In this study, we propose GPRNet, a novel geometry-aware segmentation framework that leverages geometric priors and cross-stage semantic alignment for more precise land-cover classification. Central to our approach is the Geometric Prior-Refined Block (GPRB), which learns directional derivative filters, initialized with Sobel-like operators, to generate edge-aware strength and orientation maps that explicitly encode structural cues. These maps are used to guide structure-aware attention modulation, enabling refined spatial localization. Additionally, we introduce the Mutual Calibrated Fusion Module (MCFM) to mitigate the semantic gap between encoder and decoder features by incorporating cross-stage geometric alignment and semantic enhancement mechanisms. Extensive experiments conducted on the ISPRS Potsdam and LoveDA datasets validate the effectiveness of the proposed method, with GPRNet achieving improvements of up to 1.7% mIoU on Potsdam and 1.3% mIoU on LoveDA over strong recent baselines. Furthermore, the model maintains competitive inference efficiency, suggesting a favorable balance between accuracy and computational cost. These results demonstrate the promising potential of geometric-prior integration and mutual calibration in advancing semantic segmentation in complex environments.