SALPA: Spaceborne LiDAR Point Adjustment for Enhanced GEDI Footprint Geolocation

• URL: http://arxiv.org/abs/2511.17600v1
• Date: Tue, 18 Nov 2025 03:05:30 GMT
• Title: SALPA: Spaceborne LiDAR Point Adjustment for Enhanced GEDI Footprint Geolocation
• Abstract summary: Geo geolocation uncertainties undermine forest profile estimates, including carbon stock assessments.<n>We present SALPA, a multi-algorithm optimization framework integrating three optimization paradigms with five distance metrics.<n> validation across contrasting sites: topographically complex Nikko, Japan, and flat Landes, France.
• Score: 0.052498055901649014
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Spaceborne Light Detection and Ranging (LiDAR) systems, such as NASA's Global Ecosystem Dynamics Investigation (GEDI), provide forest structure for global carbon assessments. However, geolocation uncertainties (typically 5-15 m) propagate systematically through derived products, undermining forest profile estimates, including carbon stock assessments. Existing correction methods face critical limitations: waveform simulation approaches achieve meter-level accuracy but require high-resolution LiDAR data unavailable in most regions, while terrain-based methods employ deterministic grid searches that may overlook optimal solutions in continuous solution spaces. We present SALPA (Spaceborne LiDAR Point Adjustment), a multi-algorithm optimization framework integrating three optimization paradigms with five distance metrics. Operating exclusively with globally available digital elevation models and geoid data, SALPA explores continuous solution spaces through gradient-based, evolutionary, and swarm intelligence approaches. Validation across contrasting sites: topographically complex Nikko, Japan, and flat Landes, France, demonstrates 15-16% improvements over original GEDI positions and 0.5-2% improvements over the state-of-the-art GeoGEDI algorithm. L-BFGS-B with Area-based metrics achieves optimal accuracy-efficiency trade-offs, while population-based algorithms (genetic algorithms, particle swarm optimization) excel in complex terrain. The platform-agnostic framework facilitates straightforward adaptation to emerging spaceborne LiDAR missions, providing a generalizable foundation for universal geolocation correction essential for reliable global forest monitoring and climate policy decisions.

Related papers

• ToPos: Automated Optimal Positioning on Topographic Manifolds using Constrained Geodesic Voronoi Decomposition [3.180674374101366]
  Reliable autonomous mapping, environmental last-mile logistics, and infrastructure deployment depend on the optimal surface area-balanced distribution of Spatial Reference Sites (SRS)<n>This paper introduces ToPos, an automated framework for TOPPos-aware Optimal Sampling on topographic obstructions.<n>The framework is architected as a Geographic Information System (GIS)-service to bolster the mentioned applications.
  arXiv  Detail & Related papers  (2026-09-04T12:38:48Z)
• Reinforcement Learning-Guided Evolutionary Policy Optimization for Preference-Adjustable Heterogeneous Agile Earth Observation Satellite Scheduling [50.176885352114674]
  Heterogeneous agile Earth observation satellite (AEOS) scheduling requires task selection, satellite assignment, and observation sequencing.<n>This paper proposes an evolutionary policy optimization framework for heterogeneous AEOS scheduling with preference-adjustable weighted objectives.
  arXiv  Detail & Related papers  (2026-08-25T12:16:28Z)
• A Decentralized LiDAR-SLAM System with Certifiably Optimal Pose Graph Optimization [9.471365552299678]
  This paper presents the first decentralized LiDAR-SLAM system that integrates a state-of-the-art certifiably optimal Pose Graph Optimization (PGO) backend.<n>We show that the proposed framework achieves superior robustness, improving trajectory RMSE by up to 48.9% compared to the state-of-the-arto-SLAM.
  arXiv  Detail & Related papers  (2026-05-24T12:50:53Z)
• LAGO: A Local-Global Optimization Framework Combining Trust Region Methods and Bayesian Optimization [0.0]
  We introduce LAGO, a LocAl-Global Optimization algorithm that combines gradient-enhanced Bayesian Optimization and gradient-based trust region local refinement.<n>At each iteration, global and local optimization strategies independently propose candidate points, and the next evaluation is selected based on predicted improvement.
  arXiv  Detail & Related papers  (2026-03-03T13:24:36Z)
• Breaking the Regional Barrier: Inductive Semantic Topology Learning for Worldwide Air Quality Forecasting [99.4484686548807]
  We propose OmniAir, a semantic topology learning framework tailored for global station-level prediction.<n>Our approach effectively captures long-range non-Euclidean correlations and physical diffusion patterns across unevenly distributed global networks.<n>Experiments show that OmniAir achieves state-of-the-art performance against 18 baselines, maintaining high efficiency and scalability with speeds nearly 10 times faster than existing models.
  arXiv  Detail & Related papers  (2026-01-29T15:58:07Z)
• Certifiable Alignment of GNSS and Local Frames via Lagrangian Duality [11.186187106454957]
  Esting the absolute orientation of a local system relative to a global navigation satellite system (GNSS) reference often suffers from local minima and high dependency on satellite availability.<n> methods for this alignment task rely on abundant satellites unavailable in-degraded environments, or use local optimization methods which cannot guarantee the optimality of the alignment.<n>The proposed method is certifiable, meaning it can numerically fill a gap where existing local convexs fail.
  arXiv  Detail & Related papers  (2025-12-24T04:24:33Z)
• GeoEvolve: Automating Geospatial Model Discovery via Multi-Agent Large Language Models [49.257706111340134]
  We introduce GeoEvolve, a multi-agent LLM framework that couples evolutionary search with geospatial domain knowledge.<n>We evaluate it on two fundamental and classical tasks: spatial (kriging) and spatial uncertainty.<n>It reduces spatial error (RMSE) by 13-21% and enhances uncertainty estimation performance by 17%.
  arXiv  Detail & Related papers  (2025-09-25T21:03:57Z)
• VLM-Guided Visual Place Recognition for Planet-Scale Geo-Localization [24.433604332415204]
  We propose a novel hybrid geo-localization framework that combines the strengths of vision-language models and visual place recognition.<n>We evaluate our approach on multiple geo-localization benchmarks and show that it consistently outperforms prior state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-07-23T12:23:03Z)
• RainShift: A Benchmark for Precipitation Downscaling Across Geographies [21.183274939386088]
  We introduce RainShift, a dataset and benchmark for evaluating downscaling under geographic distribution shifts.<n>We evaluate state-of-the-art downscaling approaches including GANs and diffusion models in generalizing across data gaps between the Global North and Global South.<n>Our work advances the global applicability of downscaling methods and represents a step toward reducing inequities in access to high-resolution climate information.
  arXiv  Detail & Related papers  (2025-07-07T12:25:14Z)
• Adaptive and Robust DBSCAN with Multi-agent Reinforcement Learning [53.527506374566485]
  We propose a novel Adaptive and Robust DBSCAN with Multi-agent Reinforcement Learning cluster framework, namely AR-DBSCAN.<n>We show that AR-DBSCAN not only improves clustering accuracy by up to 144.1% and 175.3% in the NMI and ARI metrics, respectively, but also is capable of robustly finding dominant parameters.
  arXiv  Detail & Related papers  (2025-05-07T11:37:23Z)
• Parameter-Efficient Adaptation of Geospatial Foundation Models through Embedding Deflection [3.805591882842849]
  DEFLECT is a novel strategy for adapting GFMs to multispectral satellite imagery with very few additional parameters.<n>It achieves on-par or higher accuracy with 5-10$times$ fewer parameters for classification and segmentation tasks.
  arXiv  Detail & Related papers  (2025-03-12T15:53:58Z)
• PSS-BA: LiDAR Bundle Adjustment with Progressive Spatial Smoothing [27.060381833488172]
  This paper presents a LiDAR bundle adjustment with progressive spatial smoothing. The effectiveness and robustness of our proposed approach have been validated on both simulation and real-world datasets.
  arXiv  Detail & Related papers  (2024-03-10T07:56:54Z)
• GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization [61.10806364001535]
  Worldwide Geo-localization aims to pinpoint the precise location of images taken anywhere on Earth. Existing approaches divide the globe into discrete geographic cells, transforming the problem into a classification task. We propose GeoCLIP, a novel CLIP-inspired Image-to-GPS retrieval approach that enforces alignment between the image and its corresponding GPS locations.
  arXiv  Detail & Related papers  (2023-09-27T20:54:56Z)
• Learning Regionalization using Accurate Spatial Cost Gradients within a Differentiable High-Resolution Hydrological Model: Application to the French Mediterranean Region [0.18139022013189662]
  Estimating distributed hydrological parameters in ungauged catchments poses a challenging regionalization problem. This paper introduces a Hybrid Assimilation and Regionalization (HDA-PR) approach incorporating learnable regionalization mappings. Results highlight a strong regionalization of HDA-PR especially in the most challenging upstream-to-downstream extrapolation scenario.
  arXiv  Detail & Related papers  (2023-08-02T07:23:50Z)
• Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation [84.0621253654014]
  We propose a framework, called BALLET, which adaptively filters for a high-confidence region of interest. We show theoretically that BALLET can efficiently shrink the search space, and can exhibit a tighter regret bound than standard BO.
  arXiv  Detail & Related papers  (2023-07-25T09:45:47Z)
• Jalisco's multiclass land cover analysis and classification using a novel lightweight convnet with real-world multispectral and relief data [51.715517570634994]
  We present our novel lightweight (only 89k parameters) Convolution Neural Network (ConvNet) to make LC classification and analysis. In this work, we combine three real-world open data sources to obtain 13 channels. Our embedded analysis anticipates the limited performance in some classes and gives us the opportunity to group the most similar.
  arXiv  Detail & Related papers  (2022-01-26T14:58:51Z)
• Localized active learning of Gaussian process state space models [63.97366815968177]
  A globally accurate model is not required to achieve good performance in many common control applications. We propose an active learning strategy for Gaussian process state space models that aims to obtain an accurate model on a bounded subset of the state-action space. By employing model predictive control, the proposed technique integrates information collected during exploration and adaptively improves its exploration strategy.
  arXiv  Detail & Related papers  (2020-05-04T05:35:02Z)

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.