Temporal window (±4 month period) around the strongest extremum point extracted from the weekly data at the daily granularity.

South Korea Geospatial Analytics Market size was valued at USD 970 Million in 2024 and is projected to reach USD 1953 Million by 2032, growing at a CAGR of 9.1% from 2026 to 2032. South Korea Geospatial Analytics Market DriversRising Government Investments in Smart City Development: The South Korea geospatial analytics market is experiencing strong growth due to increasing government funding for smart city infrastructure and digital transformation. According to the Ministry of Land, Infrastructure and Transport (2023), South Korea allocated 1.2 Trillion (USD 900 Million) for smart city projects leveraging geospatial data. Key players like SK Telecom and LG CNS have developed AI-powered geospatial platforms for urban planning. In 2024, Naver Labs launched a 3D digital twin solution for Seoul, enhancing real-time spatial analytics. Recent news highlights Samsung SDS’s partnership with local governments to integrate geospatial AI into traffic and disaster management systems.Growing Demand for Location-Based Services in Retail & Logistics: The rapid expansion of e-commerce and last-mile delivery services is driving adoption of geospatial analytics for route optimization and customer targeting. A 2023 Korea Statistics Bureau report revealed that over 65% of logistics firms now use geospatial data for fleet management. Companies like Coupang and Baemin are implementing real-time tracking systems powered by Google Maps Platform and Kakao Mobility. In early 2024, KT (Korea Telecom) introduced an AI-driven logistics analytics tool to reduce delivery times. Recent developments include Lotte Data Communication’s geofencing solutions for personalized retail marketing.Increasing Use of Geospatial Tech in Autonomous Vehicles & Drones: The push toward autonomous mobility and drone delivery is accelerating demand for high-precision geospatial mapping and analytics. The Korean Ministry of Science and ICT (2024) reported that autonomous vehicle testing zones expanded by 30% in 2023, requiring advanced spatial data. Hyundai’s Motional and Kia are collaborating with TomTom and HERE Technologies for HD mapping. In 2024, Kakao Mobility launched a drone delivery pilot in Seoul using real-time geospatial analytics. Recent news highlights Hanwha Systems’ AI-based geospatial platform for military and civilian drone operations.

The last several decades have witnessed a shift in the way in which news is delivered and consumed by users. With the growth and advancements in mobile technologies, the Internet, and Web 2.0 technologies users are not only consumers of news, but also producers of online content. This has resulted in a novel and highly participatory cyber-physical news awareness ecosystem that fosters digital activism, in which volunteers contribute content to online communities. While studies have examined the various components of this news awareness ecosystem, little is still known about how news media coverage (and in particular digital media) impacts digital activism. In order to address this challenge and develop a greater understanding of it, this paper focuses on a specific form of digital activism, that of the production of digital geographical content through crowdsourcing efforts. Using refugee camps from around the world as a case study, we examine the relationship between news coverage (via Google news), search trends (via Google trends) and user edit contribution patterns in OpenStreetMap, a prominent geospatial data crowdsourcing platform. In addition, we compare and contrast these patterns with user edit patterns in Wikipedia, a well-known non-geospatial crowdsourcing platform. Using Google news and Google trends to derive a measure of thematic public awareness, our findings indicate that digital activism bursts tend to take place during periods of sustained build-up of public awareness deficit or surplus. These findings are in line with two prominent mass communication theories: agenda setting and corrective action, and suggest the emergence of a novel stimulus-awareness-activism framework in today’s participatory digital age. Moreover, these findings further complement existing research examining the motivational factors that drive users to contribute to online collaborative communities. This paper brings us one step closer to understanding the underlying mechanisms that drive digital activism in particular in the geospatial domain.

Summary of the time gap values derived at the monthly, weekly, and daily levels for OSM and Wikipedia.

The Spain Geospatial Analytics market is booming, projected to reach €967 million by 2033 with a 9.03% CAGR. Driven by government initiatives & smart city projects, this report analyzes market trends, key players (Hexagon AB, Telespazio, Esri), and segments (surface analysis, network analysis, etc.). Discover growth opportunities in this dynamic sector. Recent developments include: January 2024 - a French location company acquires a geospatial intelligence platform based in Spain, first purchase will help us become a leading company in the UK and Europe, while still being at the forefront of innovation in the industry., November 2023 - Hexagon’s Manufacturing Intelligence branch unveiled Nexus Connected Worker, a collection of manufacturing software solutions that links employees to up-to-the-minute data for informed insights and reporting on operations, maintenance, quality, and audits. The suite offers strong integration with enterprise systems and serves as a hub for digital depictions of assets, processes, and production sites to aid in real-time decision-making., August 2023 - Maxar, a company specializing in Earth intelligence and space infrastructure, has revealed the first version of their new Geospatial Platform. The Maxar Geospatial Platform (MGP) has been created to facilitate access to the company's satellite imagery and other geospatial data, streamlining the process of discovering, purchasing, and integrating geospatial data and analytics. As of now, MGP is accessible to only certain customers, according to the news release, and Maxar anticipates that the platform will be fully ready for widespread use by the end of this year.. Key drivers for this market are: Increase in Adoption of Smart City Development and Urban Planning, Introduction of 5G to Boost Market Growth. Potential restraints include: Increase in Adoption of Smart City Development and Urban Planning, Introduction of 5G to Boost Market Growth. Notable trends are: Increase in Adoption of Smart City Development and Urban Planning.

The USGS Hydrography Community News platform serves as a key communication hub for stakeholders in the 3D Hydrography Program (3DHP). It shares updates on program initiatives, including data developments, new tool releases, and collaborative opportunities. The page frequently highlights milestones in elevation-derived hydrography (EDH) and its alignment with the broader 3D National Topography Model (3DNTM). By fostering engagement with federal, state, and local stakeholders, this platform helps promote shared understanding and innovation in hydrography.One of the featured highlights on the page is a sneak peek into HydroAdd3D, an advanced tool designed to enhance the addition and maintenance of hydrography features in 3D space. This tool integrates seamlessly with EDH workflows, enabling geospatial professionals to add, edit, and validate hydrography data within the 3DHP framework. HydroAdd3D’s capabilities provide significant improvements in accuracy and usability, allowing stakeholders to address complex hydrography challenges such as dynamic watershed changes and real-time data integration.The page also serves as a valuable resource for training and awareness, offering access to news articles, tutorials, and webinars related to the 3DHP and its associated tools. By showcasing innovative applications and providing previews of upcoming features like HydroAdd3D, the platform ensures stakeholders remain informed and equipped to leverage the latest advancements in hydrography. This collaborative approach supports the USGS mission of delivering cutting-edge geospatial tools and fostering a robust hydrography data community.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Address Ranges Feature Shapefile (ADDRFEAT.dbf) contains the geospatial edge geometry and attributes of all unsuppressed address ranges for a county or county equivalent area. The term "address range" refers to the collection of all possible structure numbers from the first structure number to the last structure number and all numbers of a specified parity in between along an edge side relative to the direction in which the edge is coded. Single-address address ranges have been suppressed to maintain the confidentiality of the addresses they describe. Multiple coincident address range feature edge records are represented in the shapefile if more than one left or right address ranges are associated to the edge. The ADDRFEAT shapefile contains a record for each address range to street name combination. Address range associated to more than one street name are also represented by multiple coincident address range feature edge records. Note that the ADDRFEAT shapefile includes all unsuppressed address ranges compared to the All Lines Shapefile (EDGES.shp) which only includes the most inclusive address range associated with each side of a street edge. The TIGER/Line shapefile contain potential address ranges, not individual addresses. The address ranges in the TIGER/Line Files are potential ranges that include the full range of possible structure numbers even though the actual structures may not exist.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Address Range Features shapefile contains the geospatial edge geometry and attributes of all unsuppressed address ranges for a county or county equivalent area. The term "address range" refers to the collection of all possible structure numbers from the first structure number to the last structure number and all numbers of a specified parity in between along an edge side relative to the direction in which the edge is coded. Single-address address ranges have been suppressed to maintain the confidentiality of the addresses they describe. Multiple coincident address range feature edge records are represented in the shapefile if more than one left or right address ranges are associated to the edge. This shapefile contains a record for each address range to street name combination. Address ranges associated to more than one street name are also represented by multiple coincident address range feature edge records. Note that this shapefile includes all unsuppressed address ranges compared to the All Lines shapefile (edges.shp) which only includes the most inclusive address range associated with each side of a street edge. The TIGER/Line shapefiles contain potential address ranges, not individual addresses. The address ranges in the TIGER/Line shapefiles are potential ranges that include the full range of possible structure numbers even though the actual structures may not exist.

Dataset

This dataset was created by Aditya Roy

Contents

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Address Ranges Feature Shapefile (ADDRFEAT.dbf) contains the geospatial edge geometry and attributes of all unsuppressed address ranges for a county or county equivalent area. The term "address range" refers to the collection of all possible structure numbers from the first structure number to the last structure number and all numbers of a specified parity in between along an edge side relative to the direction in which the edge is coded. Single-address address ranges have been suppressed to maintain the confidentiality of the addresses they describe. Multiple coincident address range feature edge records are represented in the shapefile if more than one left or right address ranges are associated to the edge. The ADDRFEAT shapefile contains a record for each address range to street name combination. Address range associated to more than one street name are also represented by multiple coincident address range feature edge records. Note that the ADDRFEAT shapefile includes all unsuppressed address ranges compared to the All Lines Shapefile (EDGES.shp) which only includes the most inclusive address range associated with each side of a street edge. The TIGER/Line shapefile contain potential address ranges, not individual addresses. The address ranges in the TIGER/Line Files are potential ranges that include the full range of possible structure numbers even though the actual structures may not exist.

This dataset is also known as the 3d layer and contains a fairly comprehensive set of unaltered source geometry polygons that overlap. It is derived from Census, State of Maine, and National Flood Hazard Layer political boundaries.rnrnThe Community Layer datasets contain geospatial community boundaries associated with Census and NFIP data. The dataset does not contain personal identifiable information (PII). The Community Layer can be used to tie Community ID numbers (CID) to jurisdiction, tribal, and special land use area boundaries.rnrnA geodatabase (GDB) link is Included in the Full Data section below. The compressed file contains a collection of files that can store, query, and manage both spatial and nonspatial data using software that can read such a file. It bcontains all of the community layers/b, not just the layer for which this dataset page describes. rnrnCitation: FEMA's citation requirements for datasets (API usage or file downloads) can be found on the OpenFEMA Terms and Conditions page, Citing Data section: https://www.fema.gov/about/openfema/terms-conditions.rnrnFor answers to Frequently Asked Questions (FAQs) about the OpenFEMA program, API, and publicly available datasets, please visit: https://www.fema.gov/about/openfema/faq.rnIf you have media inquiries about this dataset, please email the FEMA News Desk at FEMA-News-Desk@fema.dhs.gov or call (202) 646-3272. For inquiries about FEMA's data and Open Government program, please email the OpenFEMA team at OpenFEMA@fema.dhs.gov.

SDI | Data | Data Governance | News |NGA releases new data strategy to navigate digital, GEOINT revolution SPRINGFIELD, Virginia — The National Geospatial-Intelligence Agency published the agency’s data strategy Oct. 6, outlining its plans to transform and improve the way data is created, managed and shared in order to maintain dominance in the delivery of geospatial intelligence. “It is essential that we take all actions necessary to sustain our advantage in GEOINT — and that includes managing our data as a key strategic asset,’’ stated NGA Director Vice Adm. Robert Sharp in the data strategy. “With the holistic enterprise approach mapped out within this new data strategy, NGA sets forth a path for leading the way and staying ahead of our competitors.’’ The NGA Data Strategy 2021, a 28-page public document, includes both strategic goals and courses of action for the agency as it continues to chart a secure and innovative path forward while facing increasing amounts of data, risk and competition. Aligned to the agency’s Moonshot effort to “deliver trusted GEOINT with the speed, accuracy and precision required,’’ the strategy calls for the accelerated, shared and trusted use of data to help NGA better deliver on its mandates and show the way. The plan, created as a companion document to the NGA Technology Strategy published in 2020, already has played an integral role in the agency’s recent adoption of a new data governance structure to provide a coordinated framework for data policies and stewardship. The data strategy, combined with the established collaborative data governance program, guides the agency’s push to close the gap between current and future capabilities by accelerating developments in four significant focus areas: making data easily accessible, improving data reusability, improving cross-domain efficiencies and enabling next-generation GEOINT. The strategy describes four key goals being pursued by NGA to meet its mission and business needs. To achieve its desired results, the agency seeks to: — Manage data as a strategic asset: Deploy a federated enterprise data governance framework that ensures data is proactively, strategically and consistently managed while enabling agility, flexibility and innovation. Relationship to SDI'sThis reference resource provides a reference resource for SDI related activities in the intelligence community.The National Geospatial Intelligence Agency is a Federal participating organization in the Federal Geographic Data Committee. A Senior NGA Representative is a member of the FGDC Executive Committee A Senior NGA Representative is appointed by the Secretary of Interior to the National Geospatial Advisory Committee established in the Geospatial Data Act of 2018 "The head of each covered agency and the Director of the National Geospatial-Intelligence Agency shall each designate a representative of their respective agency to serve as a member of the Committee."The Geospatial Data Act of 2018 U.S.C 2804 Geospatial Standards, requires FGDC to "shall include universal data standards that shall be acceptable for the purposes of declassified intelligence community data"Additional ResourcesFederal Geographic Data CommitteeNational Geospatial Advisory CommitteeNational Geospatial Intelligence Agency National Geospatial Intelligence Agency Products and ServicesFGDC Standards

The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA.

This dataset is flattened and multicounty communities are unsplit by county lines. Flattened means that there are no overlaps; larger shapes like counties are punched out or clipped where smaller communities are contained within them. This allows for choropleth shading and other mapping techniques such as calculating unincorporated county land area. Multicounty cities like Houston are a single feature, undivided by counties. This layer is derived from Census, State of Maine, and National Flood Hazard Layer political boundaries.rnrnThe Community Layer datasets contain geospatial community boundaries associated with Census and NFIP data. The dataset does not contain personal identifiable information (PII). The Community Layer can be used to tie Community ID numbers (CID) to jurisdiction, tribal, and special land use area boundaries.rnrnA geodatabase (GDB) link is Included in the Full Data section below. The compressed file contains a collection of files that can store, query, and manage both spatial and nonspatial data using software that can read such a file. It bcontains all of the community layers/b, not just the layer for which this dataset page describes. rnThis layer can also be accessed from the FEMA ArcGIS viewer online: https://fema.maps.arcgis.com/home/item.html?id=8dcf28fc5b97404bbd9d1bc6d3c9b3cfrnrnrnCitation: FEMA's citation requirements for datasets (API usage or file downloads) can be found on the OpenFEMA Terms and Conditions page, Citing Data section: https://www.fema.gov/about/openfema/terms-conditions.rnrnFor answers to Frequently Asked Questions (FAQs) about the OpenFEMA program, API, and publicly available datasets, please visit: https://www.fema.gov/about/openfema/faq.rnIf you have media inquiries about this dataset, please email the FEMA News Desk at FEMA-News-Desk@fema.dhs.gov or call (202) 646-3272. For inquiries about FEMA's data and Open Government program, please email the OpenFEMA team at OpenFEMA@fema.dhs.gov.

World Cities provides a basemap layer of the cities for the world. The cities include national capitals, provincial capitals, major population centers, and landmark cities.Sources: Esri; Bartholemew and Times Books; U.S. Central Intelligence Agency (The World Factbook); International Organization for Standardization; United States Department of State, Bureau of Intelligence and Research; GeoNames; Executive Secretary for Foreign Names - U.S. Board on Geographic Names; U.S. National Geospatial-Intelligence Agency; BBC News; Global Mapping International

Please note that a web service is currently under development and will be launched in spring 2026. To be informed as soon as it is posted online and to stay up to date with geospatial news, subscribe to our newsletter ActuGéo. The Quebec Road Network Reference System (RQRR) includes all roads in Quebec as well as several descriptive attributes relating to the characteristics of the road, in particular the following main components: * The name of the street (odonym) * Structures * Address ranges * The name and code of the municipality * Road management * The date of creation, modification and withdrawal * Etc. The RQRR is the result of the integration of data in collaboration with the municipal sector and production partners: Élections Québec, the Ministry of Municipal Affairs and Housing, and the Ministry of Transport and Sustainable Mobility. This data includes municipal and higher road networks (highways, boulevards, etc.) geolocated. This repository is updated monthly, and is available in a single file format (flat file). The RQRR can be used in addition to the Quebec Address Repository (RQA). This third party metadata element was translated using an automated translation tool (Amazon Translate).

Dataset name

UNIFORM SEN1-2

Year of publication

2025

Author

Shamba Chowdhury Ankana Ghosh Shreyashi Ghosh

License

CC-BY-SA-4.0 The dataset contains Copernicus data (2024). Terms and conditions apply: https://scihub.copernicus.eu/twiki/pub/SciHubWebPortal/TermsConditions/TC_Sentinel_Data_31072014.pdf

Associated publication

TBA

Links

Dataset: https://www.kaggle.com/datasets/shambac/uniform-sentinel-1-2-dataset Paper: TBA

Dataset structure

• Folders named in the format of 'r_XXX' and CSV files named in the format of 'data_r_XXX.csv'.
• Each folder contains two sub folders named 's1_XXX' and 's2_XXX'.
• s1 folder contains 256x256 grayscale Sentinel 1 images from a particular region and s2 folder contains 256x256 color Sentinel 2 images from the same region.
• Each region folder has an accompanying data csv.

Dataset size

No. of files: 616,148 Storage: 53,699 MB

Description

The dataset has images spread uniformly across all over the world with 165 regions and 129,438 pairs of images. Thus the total number of image files in the dataset amounts to 258,876 images. An overview of the selected regions given on the worldmap is given in the figure below.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20405330%2F5a6633a532d3b6e3b03587781f50e1b6%2Funknown.png?generation=1757140433954325&alt=media" alt="">

The information in the CSV files are basically metadata for all the images. The information are: - Coordinates: Geo-coordinates of the top-left point of the image. - Country: Name of the country where the image was captured. - Date-Time: Date and time when the image was captured. - Resolution Scale: Geospatial resolution of the image. - Temperature Region: Temperature zone of the region in the image. - Season: Season in the specific region at the time the image was captured.

Sentinel 1 images have two more attributes to them: - Operational Mode: It is the operational/acquisition mode of the satellite it used to capture the given image. - Polarisation: It is the polarisation with which the image was captured.

Sentinel 2 images have one unique attribute: - Bands: Sentinel 2 images come with multiple different information channels called bands, this attribute contains a list of the bands in the image.

A grid of sample images from the dataset is given below:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20405330%2Fbb696ebf7c3317e0624ce84ced0b3731%2Funknown.png?generation=1757140607662415&alt=media" alt="">

Geoparsing with Large Language Models

The .zip file included in this repository contains all the code and data required to reproduce the results from our paper. Note, however, that in order to run the OpenAI models, users will required an OpenAI API key and sufficient API credits.

Data

The data used for the paper are in the datasetst and results folders.

**Datasets: **This contains the XML files (LGL and Geovirus) and Json files (News2024) used to benchmark the models. It also contains all the data used to fine-tune the gpt-3.5 model, the prompt templates sent to the LLMs, and other data used for mapping and data creation.

**Results: **This contains the results for the models on the three datastes. The folder is separated by dataset, with a single .csv file giving the results for each model on each dataset separately. The .csv file is structured so that each row contains either a predicted toponym and an associated true toponym (along with assigned spatial coordinates), if the model correctly identified a toponym; otherwise the true toponym columns are empty for false positives and the predicted columns are empty for false negatives.

Code

The code is split into two seperate folders gpt_geoparser and notebooks.

**GPT_Geoparser: **this contains the classes and methods used process the XML and JSON articles (data.py), interact with the Nominatim API for geocoding (gazetteer.py), interact with the OpenAI API (gpt_handler.py), process the outputs from the GPT models (geoparser.py) and analyse the results (analysis.py).

Notebooks: This series of notebooks can be used to reproduce the results given in the paper. The file names a reasonably descriptive of what they do within the context of the paper.

Code/software

Requirements

Numpy

Pandas

Geopy

Scitkit-learn

lxml

openai

matplotlib

Contextily

Shapely

Geopandas

tqdm

huggingface_hub

Gnews

Access information

Other publicly accessible locations of the data:

The LGL and GeoVirus datasets can also be obtained here (opens in new window).

Abstract

Geoparsing- the process of associating textual data with geographic locations - is a key challenge in natural language processing. The often ambiguous and complex nature of geospatial language make geoparsing a difficult task, requiring sophisticated language modelling techniques. Recent developments in Large Language Models (LLMs) have demonstrated their impressive capability in natural language modelling, suggesting suitability to a wide range of complex linguistic tasks. In this paper, we evaluate the performance of four LLMs - GPT-3.5, GPT-4o, Llama-3.1-8b and Gemma-2-9b - in geographic information extraction by testing them on three geoparsing benchmark datasets: GeoVirus, LGL, and a novel dataset, News2024, composed of geotagged news articles published outside the models' training window. We demonstrate that, through techniques such as fine-tuning and retrieval-augmented generation, LLMs significantly outperform existing geoparsing models. The best performing models achieve a toponym extraction F1 score of 0.985 and toponym resolution accuracy within 161 km of 0.921. Additionally, we show that the spatial information encoded within the embedding space of these models may explain their strong performance in geographic information extraction. Finally, we discuss the spatial biases inherent in the models' predictions and emphasize the need for caution when applying these techniques in certain contexts.

Methods

This contains the data and codes required to reproduce the results from our paper. The LGL and GeoVirus datasets are pre-existing datasets, with references given in the manuscript. The News2024 dataset was constructed specifically for the paper.

To construct the News2024 dataset, we first created a list of 50 cities from around the world which have population greater than 1000000. We then used the GNews python package https://pypi.org/project/gnews/ (opens in new window) to find a news article for each location, published between 2024-05-01 and 2024-06-30 (inclusive). Of these articles, 47 were found to contain toponyms, with the three rejected articles referring to businesses which share a name with a city, and which did not otherwise mention any place names.

We used a semi autonmous approach to geotagging the articles. The articles were first processed using a Distil-BERT model, fine tuned for named entity recognicion. This provided a first estimate of the toponyms within the text. A human reviewer then read the articles, and accepted or rejected the machine tags, and added any tags missing from the machine tagging process. We then used OpenStreetMap to obtain geographic coordinates for the location, and to identify the toponym type (e.g. city, town, village, river etc). We also flagged if the toponym was acting as a geo-political entity, as these were reomved from the analysis process. In total, 534 toponyms were identified in the 47 news articles.

Please note that a web service is currently under development and will be launched in winter 2026. To be informed as soon as it is posted online and to stay up to date with geospatial news, subscribe to our newsletter ActuGéo. The Quebec Address Repository (RQA) includes all municipal addresses in Quebec as well as several attributes associated with them, including the following main components: * The municipal number * The suffix * The unit (apartment/office/premises) * The name of the street (odonym) * The name and code of the municipality * The MO postal code * The main use (category) * The date of creation, modification and withdrawal * Latitude and longitude * Etc. This repository of geolocated addresses is updated monthly, and is available in a single file format (flat file). The RQA is the result of the integration of data in collaboration with the municipal sector and production partners: Élections Québec, the Ministry of Municipal Affairs and Housing, and the Ministry of Transport and Sustainable Mobility. The RQA can be used in addition to the Quebec Road Network Framework (RQRR). * “Postal Code” is an official trademark of Canada Post Corporation***This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Expedited Level 1B Registered Radiance at the Sensor global data product is radiometrically calibrated and geometrically co-registered. Application of intra-telescope and inter-telescope registration corrections for all bands are relative to the reference band for each telescope: Visible and Near Infrared (VNIR) Band 2, Shortwave Infrared (SWIR) Band 6, and Thermal Infrared (TIR) Band 11. The Expedited Level 1B data product is similar to the (AST_L1B) with a few notable exceptions. These include: * The AST_L1BE is available for download within 48 hours of acquisition in support of field calibration and validation efforts, in addition to emergency response for natural disasters where the quick turn-around time from acquisition to availability would prove beneficial in initial damage or impact assessments. * The registration quality of the AST_L1BE is likely to be lower than the AST_L1B, and may vary from scene to scene. * The AST_L1BE dataset does not contain the VNIR 3B (aft-viewing) Band. * This dataset does not have short-term calibration for the Thermal Infrared (TIR) sensor.

Known Issues * TIR bands: Acquisitions for TIR bands ended on January 16, 2026, at 05:10:45 UTC, when the ASTER TIR instrument was permanently turned off due to power limitations on the Terra spacecraft. More information is available in this NASA Science News Brief. * Data acquisition gaps: On November 28, 2024, one of Terra's power-transmitting shunt units failed. As a result, there was insufficient power to maintain functionality of the ASTER instrument. ASTER resumed acquisitions for the VNIR bands on January 18, 2025, and for the TIR bands on April 15, 2025. Users should note the data gap in ASTER acquisitions from November 28, 2024, through January 16, 2025, for VNIR observations, and a gap from November 28, 2024, through April 15, 2025, for TIR acquisitions. * SWIR bands: ASTER SWIR detectors are no longer functioning as of January 12, 2009, due to anomalously high SWIR detector temperatures. * SWIR anomaly: Users are advised that ASTER SWIR data acquired from April 2008 to January 11, 2009, exhibit anomalous saturation of values and anomalous striping. This effect is also present for some prior acquisition periods. Please refer to the ASTER SWIR User Advisory for more details.