No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data

• URL: http://arxiv.org/abs/2502.06831v1
• Date: Wed, 05 Feb 2025 16:51:13 GMT
• Title: No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data
• Authors: Daniel Cai, Randall Balestriero,
• Abstract summary: Implicit neural representations (INRs) exhibit growing promise in addressing Earth representation challenges. Existing methods disproportionately prioritize global average performance. We introduce FAIR-Earth: a first-of-its-kind dataset to examine and challenge inequities in Earth representations.
• Score: 13.412573082645096
• License:
• Abstract: Implicit neural representations (INRs) exhibit growing promise in addressing Earth representation challenges, ranging from emissions monitoring to climate modeling. However, existing methods disproportionately prioritize global average performance, whereas practitioners require fine-grained insights to understand biases and variations in these models. To bridge this gap, we introduce FAIR-Earth: a first-of-its-kind dataset explicitly crafted to examine and challenge inequities in Earth representations. FAIR-Earth comprises various high-resolution Earth signals and uniquely aggregates extensive metadata along stratifications like landmass size and population density to assess the fairness of models. Evaluating state-of-the-art INRs across the various modalities of FAIR-Earth, we uncover striking performance disparities. Certain subgroups, especially those associated with high-frequency signals (e.g., islands, coastlines), are consistently poorly modeled by existing methods. In response, we propose spherical wavelet encodings, building on previous spatial encoding research. Leveraging the multi-resolution capabilities of wavelets, our encodings yield consistent performance over various scales and locations, offering more accurate and robust representations of the biased subgroups. These open-source contributions represent a crucial step towards the equitable assessment and deployment of Earth INRs.

Related papers

• Foundation Models for Remote Sensing and Earth Observation: A Survey [101.77425018347557]
  This survey systematically reviews the emerging field of Remote Sensing Foundation Models (RSFMs) It begins with an outline of their motivation and background, followed by an introduction of their foundational concepts. We benchmark these models against publicly available datasets, discuss existing challenges, and propose future research directions.
  arXiv  Detail & Related papers  (2024-10-22T01:08:21Z)
• MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling [68.69647625472464]
  Downscaling, a crucial task in meteorological forecasting, enables the reconstruction of high-resolution meteorological states for target regions. Previous downscaling methods lacked tailored designs for meteorology and encountered structural limitations. We propose a novel model called MambaDS, which enhances the utilization of multivariable correlations and topography information.
  arXiv  Detail & Related papers  (2024-08-20T13:45:49Z)
• Generative Adversarial Models for Extreme Geospatial Downscaling [0.0]
  This paper describes a conditional GAN-based geospatial downscaling method that can accommodate very high scaling factors. The method explicitly considers the uncertainty inherent to the downscaling process that tends to be ignored in existing methods. It produces a multitude of plausible high-resolution samples instead of one single deterministic result.
  arXiv  Detail & Related papers  (2024-02-21T18:25:04Z)
• Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method [66.80344502790231]
  We extend meteorological downscaling to arbitrary scattered station scales and establish a new benchmark and dataset. Inspired by data assimilation techniques, we integrate observational data into the downscaling process, providing multi-scale observational priors. Our proposed method outperforms other specially designed baseline models on multiple surface variables.
  arXiv  Detail & Related papers  (2024-01-22T14:02:56Z)
• SatDM: Synthesizing Realistic Satellite Image with Semantic Layout Conditioning using Diffusion Models [0.0]
  Denoising Diffusion Probabilistic Models (DDPMs) have demonstrated significant promise in synthesizing realistic images from semantic layouts. In this paper, a conditional DDPM model capable of taking a semantic map and generating high-quality, diverse, and correspondingly accurate satellite images is implemented. The effectiveness of our proposed model is validated using a meticulously labeled dataset introduced within the context of this study.
  arXiv  Detail & Related papers  (2023-09-28T19:39:13Z)
• Implicit neural representation for change detection [15.741202788959075]
  Most commonly used approaches to detecting changes in point clouds are based on supervised methods. We propose an unsupervised approach that comprises two components: Implicit Neural Representation (INR) for continuous shape reconstruction and a Gaussian Mixture Model for categorising changes. We apply our method to a benchmark dataset comprising simulated LiDAR point clouds for urban sprawling.
  arXiv  Detail & Related papers  (2023-07-28T09:26:00Z)
• A General Purpose Neural Architecture for Geospatial Systems [142.43454584836812]
  We present a roadmap towards the construction of a general-purpose neural architecture (GPNA) with a geospatial inductive bias. We envision how such a model may facilitate cooperation between members of the community.
  arXiv  Detail & Related papers  (2022-11-04T09:58:57Z)
• Earthformer: Exploring Space-Time Transformers for Earth System Forecasting [27.60569643222878]
  We propose Earthformer, a space-time Transformer for Earth system forecasting. The Transformer is based on a generic, flexible and efficient space-time attention block, named Cuboid Attention. Experiments on two real-world benchmarks about precipitation nowcasting and El Nino/Southerntemporaltion show Earthformer achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-07-12T20:52:26Z)
• A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts [0.5906031288935515]
  Generative adversarial networks (GANs) have been demonstrated by the computer vision community to be successful at super-resolution problems. We show that GANs and VAE-GANs can match the statistical properties of state-of-the-art pointwise post-processing methods whilst creating high-resolution, spatially coherent precipitation maps.
  arXiv  Detail & Related papers  (2022-04-05T07:19:42Z)
• A Multi-Semantic Metapath Model for Large Scale Heterogeneous Network Representation Learning [52.83948119677194]
  We propose a multi-semantic metapath (MSM) model for large scale heterogeneous representation learning. Specifically, we generate multi-semantic metapath-based random walks to construct the heterogeneous neighborhood to handle the unbalanced distributions. We conduct systematical evaluations for the proposed framework on two challenging datasets: Amazon and Alibaba.
  arXiv  Detail & Related papers  (2020-07-19T22:50:20Z)
• HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery [55.253395881190436]
  Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem. This is important for satellite monitoring of human impact on the planet. We present HighRes-net, the first deep learning approach to MFSR that learns its sub-tasks in an end-to-end fashion.
  arXiv  Detail & Related papers  (2020-02-15T22:17:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.