Science Archaeology 3D map of Easter Island takes you places visitors aren't allowed One of the world's most isolated islands is open to virtual tourists. Breakthroughs, discoveries, and DIY tips sent every weekday. Nestled in the South Pacific Ocean, some 6,000 people live on the most isolated, inhabited island in the world: Rapa Nui. Known to many as Easter Island, a name Dutch explorer Jacob Roggeveen coined after landing on the island on Easter Sunday 1722, Rapa Nui is roughly double the size of Disney World, or 63.2 square miles. And every year, some 100,000 people visit the remote island to see the famed 13-foot-tall moai statues or Easter Island heads .

This paper introduces ISS-Geo142, a curated benchmark for geolocating astronaut photography captured from the International Space Station (ISS). Although the ISS position at capture time is known precisely, the specific Earth locations depicted in these images are typically not directly georeferenced, making automated localization non-trivial. ISS-Geo142 consists of 142 images with associated metadata and manually determined geographic locations, spanning a range of spatial scales and scene types. On top of this benchmark, we implement and evaluate three geolocation pipelines: a neural network based approach (NN-Geo) using VGG16 features and cross-correlation over map-derived Areas of Interest (AOIs), a Scale-Invariant Feature Transform based pipeline (SIFT-Match) using sliding-window feature matching on stitched high-resolution AOIs, and TerraByte, an AI system built around a GPT-4 model with vision capabilities that jointly reasons over image content and ISS coordinates. On ISS-Geo142, NN-Geo achieves a match for 75.52\% of the images under our evaluation protocol, SIFT-Match attains high precision on structurally rich scenes at substantial computational cost, and TerraByte establishes the strongest overall baseline, correctly geolocating approximately 90\% of the images while also producing human-readable geographic descriptions. The methods and experiments were originally developed in 2023; this manuscript is a revised and extended version that situates the work relative to subsequent advances in cross-view geo-localization and remote-sensing vision--language models. Taken together, ISS-Geo142 and these three pipelines provide a concrete, historically grounded benchmark for future work on ISS image geolocation.

We propose a new method named LoD-Loc for visual localization in the air.

In this paper, we introduce a novel approach to fine-grained cross-view geo-localization.

Recently, Foursquare released a global dataset with more than 100 million points of interest (POIs), each representing a real-world business on its platform. However, many entries lack complete metadata such as addresses or categories, and some correspond to non-existent or fictional locations. In contrast, OpenStreetMap (OSM) offers a rich, user-contributed POI dataset with detailed and frequently updated metadata, though it does not formally verify whether a POI represents an actual business. In this data paper, we present a methodology that integrates the strengths of both datasets: Foursquare as a comprehensive baseline of commercial POIs and OSM as a source of enriched metadata. The combined dataset totals approximately 1 TB. While this full version is not publicly released, we provide filtered releases with adjustable thresholds that reduce storage needs and make the data practical to download and use across domains. We also provide step-by-step instructions to reproduce the full 631 GB build. Record linkage is achieved by computing name similarity scores and spatial distances between Foursquare and OSM POIs. These measures identify and retain high-confidence matches that correspond to real businesses in Foursquare, have representations in OSM, and show strong name similarity. Finally, we use this filtered dataset to construct a graph-based representation of POIs enriched with attributes from both sources, enabling advanced spatial analyses and a range of downstream applications.

Road potholes pose significant safety hazards and maintenance challenges, particularly on India's diverse and under-maintained road networks. This paper presents iWatchRoadv2, a fully automated end-to-end platform for real-time pothole detection, GPS-based geotagging, and dynamic road health visualization using OpenStreetMap (OSM). We curated a self-annotated dataset of over 7,000 dashcam frames capturing diverse Indian road conditions, weather patterns, and lighting scenarios, which we used to fine-tune the Ultralytics YOLO model for accurate pothole detection. The system synchronizes OCR-extracted video timestamps with external GPS logs to precisely geolocate each detected pothole, enriching detections with comprehensive metadata, including road segment attribution and contractor information managed through an optimized backend database. iWatchRoadv2 introduces intelligent governance features that enable authorities to link road segments with contract metadata through a secure login interface. The system automatically sends alerts to contractors and officials when road health deteriorates, supporting automated accountability and warranty enforcement. The intuitive web interface delivers actionable analytics to stakeholders and the public, facilitating evidence-driven repair planning, budget allocation, and quality assessment. Our cost-effective and scalable solution streamlines frame processing and storage while supporting seamless public engagement for urban and rural deployments. By automating the complete pothole monitoring lifecycle, from detection to repair verification, iWatchRoadv2 enables data-driven smart city management, transparent governance, and sustainable improvements in road infrastructure maintenance. The platform and live demonstration are accessible at https://smlab.niser.ac.in/project/iwatchroad.

Developing universal Positioning, Navigation, and Timing (PNT) is our enduring goal. Today's complex environments demand PNT that is more resilient, energy - efficient and cognitively capable. This paper asks how we can endow unmanned systems with brain - inspired spatial cogniti on navigation while exploiting the h igh precision of machine PNT to advance universal PNT. We provide a new perspective and roadmap for shifting PNT from "tool - or iented " to "cogniti on - driven ". Contributions: (1) multi - level dissection of differences among traditional PNT, biological brain PN T and brain - inspired PNT; (2) a four - layer (observation - c apability - decision - hardware) fusion framework that unites numerical precision and brain - inspired intelligence; (3) forward - looking recommendations for future development of brain - inspired PNT . Keywords: Brain - inspired n avigation, PNT, Differences and Conjunction, Fusion F ramework 1. Introduction Unmanned system P ositioning, N avigation, and T iming (PNT) technologies have achieved numerous practical advance s. Particularly noteworthy is the rapid maturation of Global Navigation Satellite System (GNSS) - based PNT, which has not only expanded its application domains but also driven down operational costs. However, these technologies still face formidable challenges in highly uncertain and complex scenarios, such as deep s pace, the deep ocean, polar regions, and dense urban environments.

Abstract--Accurate and up-to-date geospatial information is crucial for enhancing the safety and autonomy of Inland Waterway Transport (IWT). These challenges lead to significant localization drift and produce point cloud maps lacking the semantic richness required for autonomous decision-making. This paper introduces a comprehensive LiDAR odometry and Mapping framework for inland waterway navigation (Inland-LOAM). We present an improved feature extraction method adapted to unique waterway geometries, combined with a joint optimization that incorporates the water surface as a global planar constraint to mitigate drift. We also propose an innovative pipeline that transforms dense 3D point cloud outputs into structured 2D semantic maps. By constructing semantic voxel grids and performing geometric analyses (roughness, planarity, and slope), our system classifies the environment into meaningful structural categories and supports real-time computation of critical parameters like vertical bridge clearances. An automated module then efficiently extracts shoreline boundaries, exporting them into a lightweight, IENC-compatible format. Extensive evaluations on a diverse, real-world dataset demonstrate that Inland-LOAM achieves superior localization accuracy over state-of-the-art methods. The generated maps and shorelines align with real-world conditions, providing reliable information to enhance navigational situational awareness. Both the dataset and the algorithm are publicly available to support future research. IWT constitutes an essential component of Europe's freight infrastructure, spanning a network exceeding 41,000 km, interlinking major cities and industrial hubs across 13 interconnected Member States [1]. As efforts increase to shift freight from congested road and rail networks, the importance of accurate geospatial information and detailed environmental models for managing and navigating these waterways grows [2]. Zhongbi Luo, Peter Slaets, Jan Swevers and Herman Bruyninckx are with the Division of Robotics, Automation and Mechatronics in the Department of Mechanical Engineering, KU Leuven, 3001 Leu-ven, Belgium (e-mail: zhongbi.luo@kuleuven.be;

Parking spots are essential components, providing vital mobile resources for residents in a city. Accurate Global Positioning System (GPS) points of parking spots are the core data for subsequent applications,e.g., parking management, parking policy, and urban development. However, high-rise buildings tend to cause GPS points to drift from the actual locations of parking spots; besides, the standard lower-cost GPS equipment itself has a certain location error. Therefore, it is a non-trivial task to correct a few wrong GPS points from a large number of parking spots in an unsupervised approach. In this paper, motivated by the physical constraints of parking spots (i.e., parking spots are parallel to the sides of roads), we propose an unsupervised low-rank method to effectively rectify errors in GPS points and further align them to the parking spots in a unified framework. The proposed unconventional rectification and alignment method is simple and yet effective for any type of GPS point errors. Extensive experiments demonstrate the superiority of the proposed method to solve a practical problem. The data set and the code are publicly accessible at:https://github.com/pangjunbiao/ITS-Parking-spots-Dataset.

We propose a new method named LoD-Loc for visual localization in the air.