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Prioritizing Potential Wetland Areas via Region-to-Region Knowledge Transfer and Adaptive Propagation

Yoonhyuk Choi, Reepal Shah, John Sabo, K. Selcuk Candan, Huan Liu

TL;DR

This work tackles wetland prioritization under data sparsity by introducing PoTA, which combines region-to-region knowledge transfer with adaptive propagation to overcome global and local context mismatches. It leverages domain disentanglement to separate domain-specific from shareable features and an adaptive, signed propagation mechanism to enrich regional features without overwhelming sparse signals. The approach is theoretically grounded and empirically validated across six US regions, showing gains in both accuracy and recall, particularly for sparse regions, and is supported by extensive ablations. The proposed framework has practical implications for identifying and prioritizing potential wetlands in arid and semi-arid areas where data are scarce, enabling more informed conservation and development decisions.

Abstract

Wetlands are important to communities, offering benefits ranging from water purification, and flood protection to recreation and tourism. Therefore, identifying and prioritizing potential wetland areas is a critical decision problem. While data-driven solutions are feasible, this is complicated by significant data sparsity due to the low proportion of wetlands (3-6\%) in many areas of interest in the southwestern US. This makes it hard to develop data-driven models that can help guide the identification of additional wetland areas. To solve this limitation, we propose two strategies: (1) The first of these is knowledge transfer from regions with rich wetlands (such as the Eastern US) to sparser regions (such as the Southwestern area with few wetlands). Recognizing that these regions are likely to be very different from each other in terms of soil characteristics, population distribution, and land use, we propose a domain disentanglement strategy that identifies and transfers only the applicable aspects of the learned model. (2) We complement this with a spatial data enrichment strategy that relies on an adaptive propagation mechanism. This mechanism differentiates between node pairs that have positive and negative impacts on each other for Graph Neural Networks (GNNs). To summarize, given two spatial cells belonging to different regions, we identify domain-specific and domain-shareable features, and, for each region, we rely on adaptive propagation to enrich features with the features of surrounding cells. We conduct rigorous experiments to substantiate our proposed method's effectiveness, robustness, and scalability compared to state-of-the-art baselines. Additionally, an ablation study demonstrates that each module is essential in prioritizing potential wetlands, which justifies our assumption.

Prioritizing Potential Wetland Areas via Region-to-Region Knowledge Transfer and Adaptive Propagation

TL;DR

This work tackles wetland prioritization under data sparsity by introducing PoTA, which combines region-to-region knowledge transfer with adaptive propagation to overcome global and local context mismatches. It leverages domain disentanglement to separate domain-specific from shareable features and an adaptive, signed propagation mechanism to enrich regional features without overwhelming sparse signals. The approach is theoretically grounded and empirically validated across six US regions, showing gains in both accuracy and recall, particularly for sparse regions, and is supported by extensive ablations. The proposed framework has practical implications for identifying and prioritizing potential wetlands in arid and semi-arid areas where data are scarce, enabling more informed conservation and development decisions.

Abstract

Wetlands are important to communities, offering benefits ranging from water purification, and flood protection to recreation and tourism. Therefore, identifying and prioritizing potential wetland areas is a critical decision problem. While data-driven solutions are feasible, this is complicated by significant data sparsity due to the low proportion of wetlands (3-6\%) in many areas of interest in the southwestern US. This makes it hard to develop data-driven models that can help guide the identification of additional wetland areas. To solve this limitation, we propose two strategies: (1) The first of these is knowledge transfer from regions with rich wetlands (such as the Eastern US) to sparser regions (such as the Southwestern area with few wetlands). Recognizing that these regions are likely to be very different from each other in terms of soil characteristics, population distribution, and land use, we propose a domain disentanglement strategy that identifies and transfers only the applicable aspects of the learned model. (2) We complement this with a spatial data enrichment strategy that relies on an adaptive propagation mechanism. This mechanism differentiates between node pairs that have positive and negative impacts on each other for Graph Neural Networks (GNNs). To summarize, given two spatial cells belonging to different regions, we identify domain-specific and domain-shareable features, and, for each region, we rely on adaptive propagation to enrich features with the features of surrounding cells. We conduct rigorous experiments to substantiate our proposed method's effectiveness, robustness, and scalability compared to state-of-the-art baselines. Additionally, an ablation study demonstrates that each module is essential in prioritizing potential wetlands, which justifies our assumption.
Paper Structure (26 sections, 2 theorems, 11 equations, 6 figures, 6 tables)

This paper contains 26 sections, 2 theorems, 11 equations, 6 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Challenges and Solutions
  3. Contributions of this Paper
  4. Related Work
  5. Wetland Identification
  6. Transfer Learning
  7. Adaptive Propagation
  8. Preliminaries
  9. Land Cover and Surface Features
  10. Problem Formulation
  11. Methodology
  12. Feature Processing and Extraction
  13. Soil Data
  14. HAND Data
  15. Transfer Learning
  16. ...and 11 more sections

Key Result

Theorem 4.1

Let us assume a domain identifier $\mathcal{D} \in \{\mathcal{S},\mathcal{T}\}$. Regardless of a specific domain $\mathcal{D}$, the mutual information (I) between the domain-common feature $l^*_{com}$ and domain-specific one $l^*_{spe}$hwang2020variational can be decomposed as below: Then, we get $I(\mathcal{D};l^*_{spe},l^*_{com})=I(\mathcal{D};l^*_{spe})+I(\mathcal{D};l^*_{com})-I(l^*_{spe};l^*

Figures (6)

  • Figure 1: Natural Land Cover Dataset (NLCD dewitz2021national provides 20 categories of land use, including wetlands (types 90 and 95). The data reveals that only 6% of the land in the US is wetland
  • Figure 2: Knowledge transfer from wetland-rich areas of the US to regions where wetlands are inherently sparse
  • Figure 3: Overview of the proposed Potential wetlands via Transfer learning and Adaptive propagation (PoTA)
  • Figure 4: (RQ1) While knowledge transfer always provides gains, sparser regions benefit most from being the target, whereas denser regions are best used as the source domains
  • Figure 5: (RQ2) Accuracy impact of (a) using domain-specific vs. domain-shareable features and (b) adaptive propagation
  • ...and 1 more figures

Theorems & Definitions (4)

  • definition 1: Wetland Classification
  • definition 2: Wetland Classification with Knowledge Transfer
  • Theorem 4.1: Domain disentanglement
  • Theorem 4.2: Adaptive propagation