LIGHTHOUSE: Fast and precise distance to shoreline calculations from anywhere on earth

• URL: http://arxiv.org/abs/2506.18842v1
• Date: Mon, 23 Jun 2025 17:00:34 GMT
• Title: LIGHTHOUSE: Fast and precise distance to shoreline calculations from anywhere on earth
• Authors: Patrick Beukema, Henry Herzog, Yawen Zhang, Hunter Pitelka, Favyen Bastani,
• Abstract summary: We introduce a new dataset and algorithm for fast and efficient coastal distance calculations from Anywhere on Earth (AoE)<n>We provide a global coastline dataset at 10 meter resolution, a 100+ fold improvement in precision over existing data.<n>To handle the computational challenge of querying at such an increased scale, we introduce a new library: Layered Iterative Geospatial Hierarchical Terrain-Oriented Unified Search Engine (Lighthouse)
• Score: 4.841249373076213
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a new dataset and algorithm for fast and efficient coastal distance calculations from Anywhere on Earth (AoE). Existing global coastal datasets are only available at coarse resolution (e.g. 1-4 km) which limits their utility. Publicly available satellite imagery combined with computer vision enable much higher precision. We provide a global coastline dataset at 10 meter resolution, a 100+ fold improvement in precision over existing data. To handle the computational challenge of querying at such an increased scale, we introduce a new library: Layered Iterative Geospatial Hierarchical Terrain-Oriented Unified Search Engine (Lighthouse). Lighthouse is both exceptionally fast and resource-efficient, requiring only 1 CPU and 2 GB of RAM to achieve millisecond online inference, making it well suited for real-time applications in resource-constrained environments.

Related papers

• Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction [5.6087513714958686]
  Current technology allows for hourly temperature observations at 2 km, but only every 16 days at 100 m, a gap further exacerbated by cloud cover.<n>Here, we present a physics-guided deep learning framework for temperature data reconstruction that integrates two data sources.<n>The proposed framework uses a convolutional neural network that incorporates the annual temperature cycle and includes a linear term to amplify the coarse Earth system model output into fine-scale temperature values observed from satellites.
  arXiv  Detail & Related papers  (2025-07-14T03:03:25Z)
• GeoLLM: Extracting Geospatial Knowledge from Large Language Models [49.20315582673223]
  We present GeoLLM, a novel method that can effectively extract geospatial knowledge from large language models. We demonstrate the utility of our approach across multiple tasks of central interest to the international community, including the measurement of population density and economic livelihoods. Our experiments reveal that LLMs are remarkably sample-efficient, rich in geospatial information, and robust across the globe.
  arXiv  Detail & Related papers  (2023-10-10T00:03:23Z)
• LaMAR: Benchmarking Localization and Mapping for Augmented Reality [80.23361950062302]
  We introduce LaMAR, a new benchmark with a comprehensive capture and GT pipeline that co-registers realistic trajectories and sensor streams captured by heterogeneous AR devices. We publish a benchmark dataset of diverse and large-scale scenes recorded with head-mounted and hand-held AR devices.
  arXiv  Detail & Related papers  (2022-10-19T17:58:17Z)
• OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping [15.419052489797775]
  OpenEarthMap is a benchmark dataset for global high-resolution land cover mapping. It consists of 2.2 million segments of 5000 aerial and satellite images covering 97 regions from 44 countries across 6 continents.
  arXiv  Detail & Related papers  (2022-10-19T17:20:16Z)
• Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization [73.96101108943986]
  A Needle-in-a-Haystack problem arises when there is an extreme imbalance of optimum conditions relative to the size of the dataset. We present a Zooming Memory-Based Initialization algorithm that builds on conventional Bayesian optimization principles.
  arXiv  Detail & Related papers  (2022-08-26T23:57:41Z)
• Open High-Resolution Satellite Imagery: The WorldStrat Dataset -- With Application to Super-Resolution [3.5626735456895964]
  We introduce here the WorldStrat dataset.<n>The largest and most varied such publicly available dataset, at Airbus SPOT 6/7 satellites' high resolution of up to 1.5 m/pixel.<n>We temporally-match each high-resolution image with multiple low-resolution images from the freely accessible lower-resolution Sentinel-2 satellites at 10 m/pixel.
  arXiv  Detail & Related papers  (2022-07-13T14:30:20Z)
• Satellite Image Time Series Analysis for Big Earth Observation Data [50.591267188664666]
  This paper describes sits, an open-source R package for satellite image time series analysis using machine learning. We show that this approach produces high accuracy for land use and land cover maps through a case study in the Cerrado biome.
  arXiv  Detail & Related papers  (2022-04-24T15:23:25Z)
• Deep Learning for Real Time Satellite Pose Estimation on Low Power Edge TPU [58.720142291102135]
  In this paper we propose a pose estimation software exploiting neural network architectures. We show how low power machine learning accelerators could enable Artificial Intelligence exploitation in space.
  arXiv  Detail & Related papers  (2022-04-07T08:53:18Z)
• Deep Learning Aided Packet Routing in Aeronautical Ad-Hoc Networks Relying on Real Flight Data: From Single-Objective to Near-Pareto Multi-Objective Optimization [79.96177511319713]
  We invoke deep learning (DL) to assist routing in aeronautical ad-hoc networks (AANETs) A deep neural network (DNN) is conceived for mapping the local geographic information observed by the forwarding node into the information required for determining the optimal next hop. We extend the DL-aided routing algorithm to a multi-objective scenario, where we aim for simultaneously minimizing the delay, maximizing the path capacity, and maximizing the path lifetime.
  arXiv  Detail & Related papers  (2021-10-28T14:18:22Z)
• SpaceNet 6: Multi-Sensor All Weather Mapping Dataset [13.715388432549373]
  We present an open Multi-Sensor All Weather Mapping (MSAW) dataset and challenge. MSAW covers 120 km2 over multiple overlapping collects and is annotated with over 48,000 unique building footprints labels. We present a baseline and benchmark for building footprint extraction with SAR data and find that state-of-the-art segmentation models pre-trained on optical data, and then trained on SAR.
  arXiv  Detail & Related papers  (2020-04-14T13:43:11Z)

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.