With the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few. With increasing complexity and different sensing techniques at our disposal, it has become our primary interest to design efficient algorithms to retrieve data from multiple data modalities, given the complementary information that is captured by different sensors. This type of problem is referred to as inter-modal data retrieval. In remote sensing (RS), there are primarily two important types of problems, i.e., land-cover classification and object detection. In this work, we focus on the target-based object retrieval part, which falls under the realm of object detection in RS. Object retrieval essentially requires high-resolution imagery for objects to be distinctly visible in the image. The main challenge with the conventional retrieval approach using large-scale databases is that, quite often, we do not have any query image sample of the target class at our disposal. The target of interest solely exists as a perception to the user in the form of an imprecise sketch. In such situations where a photo query is absent, it can be immensely useful if we can promptly make a quick hand-made sketch of the target. Sketches are a highly symbolic and hieroglyphic representation of data. One can exploit the notion of this minimalistic representative of sketch queries for sketch-based image retrieval (SBIR) framework. While dealing with satellite images, it is imperative to collect as many samples of images as possible for each object class for object recognition with a high success rate. However, in general, there exists a considerable number of classes for which we seldom have any training data samples. Therefore, for such classes, we can use the zero-shot learning (ZSL) strategy. The ZSL approach aims to solve a task without receiving any example of that task during the training phase. This makes the network capable of handling an unseen class (new class) sample obtained during the inference phase upon deployment of the network. In this database, we propose an aerial skecth-image database that can be useful for designing frameworks for the above-mentioned tasks.

The definitive dataset framework realized on the ArcGIS software, containing the Data Analysis on Berga, used to build the composite indicators of the HISMAGIS Protocol.

Alle the feature class, geodatabase and collector carpets have been translated in English.

Images from Sentinel 2 for dehazing. Contains 3 folders, one with original images, one with dehazed images at default exponent of 0.8 and the last with failed images with fine tuned exponent (thus becoming successful).

In response to the growing concern in geographic information science, which pertains to utilizing contemporary internet technology to communicate past information or knowledge for establishing foundations in geography. Recent studies have investigated geomatics solutions for historical city, and enhancing GIS skills through collaborative approach. In this study, we build upon prior research by exploring how the implementation of current technology can promote a cooperative learning environment, particularly within the realm of forestry education. Minetest, the 3D voxel game engine has high capability of modification, for visualizing natural environments and urban structures. The goal of this study was to investigate the potential of using the game engine for forestry education purposes. To meet this objective, we developed precise and detailed models of building structures and their surrounding environment. We also explored the visualization beyond 3D geospatial data, by generating analytical results of solar radiation on building roofs using GIS software. The visualization process was facilitated by the use of 3D light detection and ranging (LiDAR) data, provided by the UBC Campus + Community Planning department. The proposed approach proved to be effective in producing compatible geospatial data for visualization in the game engine. We also conducted exploratory statistical analysis to examine the relationship between building energy usage and solar radiation. The exploratory regression result of the solar radiation analysis has an R2adj of 0.19, which indicates its insignificant impact on building energy usage. Finally, the findings of this research provide a foundation for future studies that can continue to explore the potential of using 3D game engines. Keywords: 3D Geo-Visualization, Forestry Education, Remote Sensing, Light Detection and Ranging (LiDAR), Building Energy Usage, Solar Radiation Analysis

This dataset contains the weights of a convolutional neural network (CNN) trained to recognize the presence of solar panels on aerial photos. In particular, it contains the saved state of a ResNet50 CNN that has been trained on a dataset containing annotated high-resolution aerial images of two regions in the south of the Netherlands. Many photos in this dataset have been annotated multiple times, and the annotations are not always unanimous. The dataset of aerial images together with annotations can be downloaded from here.

The model for detecting whether solar panels are present in aerial photos has been developed under the DeepSolaris and DeepGeoStat projects. Corresponding Pytorch code can be found here. The code also demonstrates how to load the saved state into a ResNet50 model, and use it for detecting solar panels on aerial photos.

This research was conducted under:

• ESS action 'Merging Geostatistics and Geospatial Information in Member States' (grant agreement no.: 08143.2017.001-2017.408),
• ESS topic B5674-2020-GEOS (project 101033951 2020-NL-GEOS-DEEP-GEO-STAT),
• a research program of Statistics Netherlands (https://www.cbs.nl)

Market Overview:

The Vietnam geospatial analytics market size is projected to exhibit a growth rate (CAGR) of 8.90% during 2024-2032. The increasing product utilization by government authorities in various sectors, various technological advancements in satellite technology, remote sensing, and data collection methods, and the rising development of smart cities represent some of the key factors driving the market.

Report Attribute	Key Statistics
Base Year	2023
Forecast Years	2024-2032
Historical Years	2018-2023
Market Growth Rate (2024-2032)	8.90%

Geospatial analytics is a field of data analysis that focuses on the interpretation and analysis of geographic and spatial data to gain valuable insights and make informed decisions. It combines geographical information systems (GIS), advanced data analysis techniques, and visualization tools to analyze and interpret data with a spatial or geographic component. It also enables the collection, storage, analysis, and visualization of geospatial data. It provides tools and software for managing and manipulating spatial data, allowing users to create maps, perform spatial queries, and conduct spatial analysis. In addition, geospatial analytics often involves integrating geospatial data with other types of data, such as demographic data, environmental data, or economic data. This integration helps in gaining a more comprehensive understanding of complex phenomena. Moreover, geospatial analytics has a wide range of applications. For example, it can be used in urban planning to optimize transportation routes, in agriculture to manage crop yield and soil quality, in disaster management to assess and respond to natural disasters, in wildlife conservation to track animal migrations, and in business for location-based marketing and site selection.

Vietnam Geospatial Analytics Market Trends:

The Vietnamese government has recognized the importance of geospatial analytics in various sectors, including urban planning, agriculture, disaster management, and environmental monitoring. Initiatives to develop and utilize geospatial data for public projects and policy-making have spurred demand for geospatial analytics solutions. In addition, Vietnam is experiencing rapid urbanization and infrastructure development. Geospatial analytics is critical for effective urban planning, transportation management, and infrastructure optimization. This trend is driving the adoption of geospatial solutions in cities and regions across the country. Besides, Vietnam's agriculture sector is a significant driver of its economy. Geospatial analytics helps farmers and agricultural businesses optimize crop management, soil health, and resource allocation. Consequently, precision farming techniques, enabled by geospatial data, are becoming increasingly popular, which is also propelling the market. Moreover, the development of smart cities in Vietnam relies on geospatial analytics for various applications, such as traffic management, public safety, and energy efficiency. Geospatial data is central to building the infrastructure needed for smart city initiatives. Furthermore, advances in satellite technology, remote sensing, and data collection methods have made geospatial data more accessible and affordable. This has lowered barriers to entry and encouraged the use of geospatial analytics in various sectors. Additionally, the telecommunications sector in Vietnam is expanding, and location-based services, such as navigation and advertising, rely on geospatial analytics. This creates opportunities for geospatial data providers and analytics solutions in the telecommunications industry.

Vietnam Geospatial Analytics Market Segmentation:

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the country level for 2024-2032. Our report has categorized the market based on component, type, technology, enterprise size, deployment mode, and vertical.

Component Insights:

https://www.imarcgroup.com/CKEditor/2e6fe72c-0238-4598-8c62-c08c0e72a138other-regions1.webp" style="height:450px; width:800px" />

• Solution
• Services
  

The report has provided a detailed breakup and analysis of the market based on the component. This includes solution and services.

Type Insights:

• Surface and Field Analytics
• Network and Location Analytics
• Geovisualization
• Others
  

A detailed breakup and analysis of the market based on the type have also been provided in the report. This includes surface and field analytics, network and location analytics, geovisualization, and others.

Technology Insights:

• Remote Sensing
• GIS
• GPS
• Others
  

The report has provided a detailed breakup and analysis of the market based on the technology. This includes remote sensing, GIS, GPS, and others.

Enterprise Size Insights:

• Large Enterprises
• Small and Medium-sized Enterprises
  

A detailed breakup and analysis of the market based on the enterprise size have also been provided in the report. This includes large enterprises and small and medium-sized enterprises.

Deployment Mode Insights:

• On-premises
• Cloud-based
  

The report has provided a detailed breakup and analysis of the market based on the deployment mode. This includes on-premises and cloud-based.

Vertical Insights:

• Automotive
• Energy and Utilities
• Government
• Defense and Intelligence
• Smart Cities
• Insurance
• Natural Resources
• Others
  

A detailed breakup and analysis of the market based on the vertical have also been provided in the report. This includes automotive, energy and utilities, government, defense and intelligence, smart cities, insurance, natural resources, and others.

Regional Insights:

https://www.imarcgroup.com/CKEditor/bbfb54c8-5798-401f-ae74-02c90e137388other-regions6.webp" style="height:450px; width:800px" />

• Northern Vietnam
• Central Vietnam
• Southern Vietnam
  

The report has also provided a comprehensive analysis of all the major regional markets, which include Northern Vietnam, Central Vietnam, and Southern Vietnam.

Competitive Landscape:

The market research report has also provided a comprehensive analysis of the competitive landscape in the market. Competitive analysis such as market structure, key player positioning, top winning strategies, competitive dashboard, and company evaluation quadrant has been covered in the report. Also, detailed profiles of all major companies have been provided.

Vietnam Geospatial Analytics Market Report Coverage:

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Report Features	Details
Base Year of the Analysis	2023
Historical Period

Geospatial Solutions Market size was valued at USD 282.75 Billion in 2024 and is projected to reach USD 650.14 Billion by 2031, growing at a CAGR of 12.10% during the forecast period 2024-2031.

Geospatial Solutions Market: Definition/ Overview

Geospatial solutions are applications and technologies that use spatial data to address geography, location, and Earth’s surface problems. They use tools like GIS, remote sensing, GPS, satellite imagery analysis, and spatial modelling. These solutions enable informed decision-making, resource allocation optimization, asset management, environmental monitoring, infrastructure planning, and addressing challenges in sectors like urban planning, agriculture, transportation, disaster management, and natural resource management. They empower users to harness spatial information for better understanding and decision-making in various contexts.

Geospatial solutions are technologies and methodologies used to analyze and visualize spatial data, ranging from urban planning to agriculture. They use GIS, remote sensing, and GNSS to gather, process, and interpret data. These solutions help users make informed decisions, solve complex problems, optimize resource allocation, and enhance situational awareness. They are crucial in addressing challenges and unlocking opportunities in today’s interconnected world, such as mapping land use patterns, monitoring ecosystem changes, and real-time asset tracking.

United States geospatial analytics market size reached USD 25.2 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 60.1 Billion by 2033, exhibiting a growth rate (CAGR) of 10% during 2025-2033. The growing need for facilitating data-driven decisions, along with the rising focus of government bodies on improving situational awareness and monitoring of troops and enemy movements, is primarily propelling the market growth across the country.

Report Attribute	Key Statistics
Base Year	2024
Forecast Years	2025-2033
Historical Years	2019-2024
​Market Size in 2024	USD 25.2 Billion
​Market Forecast in 2033	USD 60.1 Billion
Market Growth Rate (2025-2033)	10%

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the country level for 2025-2033. Our report has categorized the market based on component, type, technology, enterprise size, deployment mode, and vertical.

Cloud-free imageries, acquired from Landsat 8 OLI during 2016 to 2018, were used to delineate the extents of the glacial lakes in the mountainous terrain of CPEC

The geospatial analytics market size is predicted to rise from $93.49 billion in 2024 to $362.45 billion by 2035, growing at a CAGR of 13.1% from 2024 to 2035.

TRACERAQ_AircraftRemoteSensing_GV_GCAS_Data is the remotely sensed GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) data collected onboard the Johnson Space Center (JSC) Gulfstream V (G-V) aircraft during the TRacking Aerosol Convection ExpeRiment – Air Quality (TRACER-AQ) field study. Data collection is ongoing. The TRacking Aerosol Convection ExpeRiment – Air Quality (TRACER-AQ) campaign is a field study co-sponsored by NASA and TCEQ (Texas Commission on Environmental Quality), with partners from DOE (Department of Energy) TRacking Aerosol Convection ExpeRiment (TRACER), and several academic institutions. This synergistic effort aims to gain an updated understanding in photochemistry and meteorological impact on ozone formation in the Houston region, particularly around the Houston Ship Channel, Galveston Bay, and the Gulf of Mexico; provide observations for evaluating air quality models and satellite observations; and identify injustices due to air quality in relation to socioeconomic factors. The primary TRACER-AQ field observations period lasted from mid-August to late September 2021, coinciding with the peak ozone season in East Texas, with a second deployment in summer 2022 with a subset of ground-based assets. The observing system includes airborne remote sensing, mobile (boat/vehicle) laboratories, and stationary ground-based assets. The airborne component was based on the NASA Gulfstream V aircraft instrumented with GCAS (GEOCAPE Airborne Simulator) for making measurements of column NO2 and HCHO as well as a lidar system, HSRL-2 (High Spectral Resolution Lidar-2), to measure O3 and aerosol vertical profiles over the course of 12 flight days. Ground-based assets include ground-based ozone lidars from the Tropospheric Ozone Lidar Network (TOLNet), ceilometers, Pandora spectrometers, AErosol RObotic NETwork (AERONET) remote sensors, ozonesondes, and stationary and mobile laboratories of in situ air quality and meteorological observations. This coordinated observing system provides updated or unseen perspectives in spatial and temporal distribution of the key photochemical species and atmospheric structure information, particularly with a focus on the temporal evolution of observations throughout the daytime in preparation for upcoming geostationary satellite air quality observations.

We introduce a new image-text dataset, providing high-quality natural language descriptions for global-scale satellite data. Specifically, we utilize Sentinel-2 data for its global coverage as the foundational image source, employing semantic segmentation labels from the European Space Agency's WorldCover project to enrich the descriptions of land covers. By conducting in-depth semantic analysis, we formulate detailed prompts to elicit rich descriptions from ChatGPT. We then include a manual verification process to enhance the dataset's quality further. This step involves manual inspection and correction to refine the dataset. Finally, we offer the community ChatEarthNet, a large-scale image-text dataset characterized by global coverage, high quality, wide-ranging diversity, and detailed descriptions. ChatEarthNet consists of 163,488 image-text pairs with captions generated by ChatGPT-3.5 and an additional 10,000 image-text pairs with captions generated by ChatGPT-4V(ision). This dataset has significant potential for both training and evaluating vision-language geo-foundation models for remote sensing.

Perpendicularly aligned N-S line (300 m) and E-W line (300 m) with copper plates

The ABI/GOES-16 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_ABI_G16 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 144 granules over the daylit hours of a 24-hour period. The Geostationary Operational Environmental Satellite – GOES-16 has been serving in the operational GOES-East position (near -75°W) since December 18, 2017. The GOES-16/ABI collection record spans from January 2019 through December 2022. The XAERDT_L2_ABI_G16 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_ABI_G16 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_ABI_G16 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/

The MODIS/Terra Dark Target Aerosol 5-Min L2 Swath 10 km product, short-name XAERDT_L2_MODIS_Terra is provided at 10-km spatial resolution (at-nadir) and a 5-minute cadence that typically yields about 140 granules over the daylit hours of a 24-hour period. The Terra/MODIS L2 collection record spans from January 2019 through December 2022. The XAERDT_L2_MODIS_Terra product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_MODIS_Terra product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_MODIS_Terra Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/

This dataset was generated by the Remote Sensing Group of the TU Wien Department of Geodesy and Geoinformation (https://mrs.geo.tuwien.ac.at/), within a dedicated project by the European Space Agency (ESA). Rights are reserved with ESA. Open use is granted under the CC BY 4.0 license.With this dataset publication, we open up a new perspective on Earth's land surface, providing a normalised microwave backscatter map from spaceborne Synthetic Aperture Radar (SAR) observations. The Sentinel-1 Global Backscatter Model (S1GBM) describes Earth for the period 2016-17 by the mean C-band radar cross section in VV- and VH-polarization at a 10 m sampling, giving a high-quality impression on surface- structures and -patterns.At TU Wien, we processed 0.5 million Sentinel-1 scenes totaling 1.1 PB and performed semi-automatic quality curation and backscatter harmonisation related to orbit geometry effects. The overall mosaic quality excels (the few) existing datasets, with minimised imprinting from orbit discontinuities and successful angle normalisation in large parts of the world. Supporting the designand verification of upcoming radar sensors, the obtained S1GBM data potentially also serve land cover classification and determination of vegetation and soil states, as well as water body mapping.We invite developers from the broader user community to exploit this novel data resource and to integrate S1GBM parameters in models for various variables of land cover, soil composition, or vegetation structure.Please be referred to our peer-reviewed article at TODO: LINK TO BE PROVIDED for details, generation methods, and an in-depth dataset analysis. In this publication, we demonstrate – as an example of the S1GBM's potential use – the mapping of permanent water bodies and evaluate the results against the Global Surface Water (GSW) benchmark.Dataset RecordThe VV and VH mosaics are sampled at 10 m pixel spacing, georeferenced to the Equi7Grid and divided into six continental zones (Africa, Asia, Europe, North America, Oceania, South America), which are further divided into square tiles of 100 km extent ("T1"-tiles). With this setup, the S1GBM consists of 16071 tiles over six continents, for VV and VH each, totaling to a compressed data volume of 2.67 TB.The tiles' file-format is a LZW-compressed GeoTIFF holding 16-bit integer values, with tagged metadata on encoding and georeference. Compatibility with common geographic information systems as QGIS or ArcGIS, and geodata libraries as GDAL is given.In this repository, we provide each mosaic as tiles that are organised in a folder structure per continent. With this, twelve zipped dataset-collections per continent are available for download.Web-Based Data ViewerIn addition to this data provision here, there is a web-based data viewer set up at the facilities of the Earth Observation Data Centre (EODC) under http://s1map.eodc.eu/. It offers an intuitive pan-and-zoom exploration of the full S1GBM VV and VH mosaics. It has been designed to quickly browse the S1GBM, providing an easy and direct visual impression of the mosaics.Code AvailabilityWe encourage users to use the open-source Python package yeoda, a datacube storage access layer that offers functions to read, write, search, filter, split and load data from the S1GBM datacube. The yeoda package is openly accessible on GitHub at https://github.com/TUW-GEO/yeoda.Furthermore, for the usage of the Equi7Grid we provide data and tools via the python package available on GitHub at https://github.com/TUW-GEO/Equi7Grid. More details on the grid reference can be found in https://www.sciencedirect.com/science/article/pii/S0098300414001629.AcknowledgementsThis study was partly funded by the project "Development of a Global Sentinel-1 Land Surface Backscatter Model", ESA Contract No. 4000122681/17/NL/MP for the European Union Copernicus Programme. The computational results presented have been achieved using the Vienna Scientific Cluster (VSC). We further would like to thank our colleagues at TU Wien and EODC for supporting us on technical tasks to cope with such a large and complex data set. Last but not least, we appreciate the kind assistance and swift support of the colleagues from the TU Wien Center for Research Data Management.

Global Geographic Information System Software Market was valued at USD 8.5 billion in 2022 and will reach USD 21.0 billion by 2030, registering a CAGR of 12.1% for the forecast period 2023-2030. Factor Impacting the Geographic Information System Software Market:

The development of smart cities and Modern urban Planning is expected to drive the Geographic Information System Software Market

The process of site selection, land acquisition, planning, designing, visualizing, building, project management, operations, and reporting are all aided by geographic information system (GIS) software for smart cities. Moreover, geographic information system (GIS) solutions are used in urban planning by experts to better properly analyze, model, and visualize places. By processing geospatial data from satellite imaging, aerial photography, and remote sensors, geographic information system (GIS) software systems offer a comprehensive perspective of the land and infrastructure. Additionally, the industry for geographic information system software is growing over the forecast period as a result of such geographic information system (GIS) software applications.

Restraining factor for Geographic Information System Software Market

The high cost of the system has impacted the Geographic Information System Software Market

The pricey geographic information system will further derail the overall market’s growth. The geographic information system (GIS) is expensive because, in addition to the technology and software, it is necessary to have a properly qualified human workforce. Moreover, Specialized knowledge is needed to comprehend and interpret the information gathered by a geographic information system (GIS) system, which is expensive to hire and train. This factor will therefore obstruct market growth over the forecast period. What is Geographic Information System Software?

Geographic Information System Software is used to develop, hold, retrieve, organize, display, and perform analyses on many kinds of spatial and geographic data. The geographic information system (GIS) Industry is majorly driven by infrastructural developments, such as smart cities, water and land management, utility, and urban planning. The services segment provides various applications such as location-based services and, thus, is one of the prominent contributors to the market share. Advancements in GIS technologies, such as geo-analytics and integrated location-based data services, are also boosting the adoption of GIS in various regional markets, thereby driving the market demand over the forecast period.

The ASEAN satellite-based Earth observation market, currently valued at approximately $0.04 billion in 2025 (estimated based on a global market size of $0.2 billion and assuming ASEAN represents a significant, yet proportionally smaller, share), is poised for robust growth, projected to exhibit a Compound Annual Growth Rate (CAGR) of 12.59% from 2025 to 2033. This expansion is driven by increasing government investments in infrastructure development, particularly in urban planning and disaster management, coupled with a rising demand for precise agricultural insights and climate change monitoring. The adoption of advanced Earth observation technologies, including high-resolution satellite imagery and sophisticated data analytics, is further fueling market growth. Key segments driving this growth include value-added services (data processing and analysis), and applications in urban development, agriculture, and climate services. Low Earth Orbit (LEO) satellites are expected to dominate the satellite orbit segment due to their superior image resolution and data acquisition capabilities. However, market growth faces some challenges. High initial investment costs associated with satellite technology and data acquisition can hinder adoption, particularly among smaller businesses and organizations. Furthermore, data security and privacy concerns, along with the need for skilled professionals to interpret and utilize complex Earth observation data, represent potential restraints on market expansion. Despite these challenges, the long-term outlook remains positive, fueled by ongoing technological advancements, increasing data accessibility, and a growing awareness of the crucial role Earth observation plays in sustainable development and resource management within the dynamic ASEAN region. The presence of key players like Airbus SE, Maxar Technologies, and Spire Global, coupled with burgeoning local companies such as EOMAP Indonesia, signals a vibrant and competitive market. Recent developments include: February 2023: An MOU was formed to establish an international space collaboration between the Geo-Informatics and Space Technology Development Agency (GISTDA) of Thailand and the Office for Space Technology & Industry, Singapore (OSTIn) to develop a framework for close collaboration on Earth observation (EO) applications between the two countries of the ASEAN region, which would create an opportunity for the market vendors., May 2023: Airbus planned its satellite service expansion in Malaysia due to the strong potential in Malaysia's Space sector and strategized to contribute further to the country's sovereign Space development journey with its additional satellite launched in June 2022. Airbus and MYSA have been partnered in space collaborators in the areas of satellite imagery, systems, and services, which includes Airbus' collaboration with MYSA for the constellation of Earth observation satellites entirely funded, manufactured, owned, and operated by Airbus, enabling MYSA to use the satellite-based Earth observation services.. Key drivers for this market are: Government Initiatives and Investments, Technological Advancements. Potential restraints include: Budget Constraints and Technological Limitations, Regulatory and Legal Challenges. Notable trends are: Government Initiatives and Investments is Driving the Market.

The VIIRS/NOAA20 L2 Dark Target Aerosol 6-Min L2 Swath 6 km product, short-name XAERDT_L2_VIIRS_NOAA20 is provided at 6-km spatial resolution (at-nadir) and a 6-minute cadence that typically yields about 130 granules over the daylit hours of a 24-hour period. The NOAA20/VIIRS L2 collection record spans from January 2019 through December 2022.

The XAERDT_L2_VIIRS_NOAA20 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites.

The XAERDT_L2_VIIRS_NOAA20 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs.

For more information consult LAADS product description page at:

https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_VIIRS_NOAA20

Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/

The global remote sensing services market size reached US$ 15.7 billion in 2022. Revenue generated by remote sensing system sales is likely to be US$ 18.4 billion in 2023. Sales are poised to soar by 14.0% CAGR over the forecast period between 2023 and 2033. Demand is anticipated to transcend at US$ 68.0 billion by 2033 end.

Attributes	Key Insights
Remote Sensing Services Market Estimated Size (2023E)	US$ 18.4 billion
Remote Sensing Services Market Projected Valuation (2033F)	US$ 68.0 billion
Value-based CAGR (2023 to 2033)	14.0%

Historical Performance of Remote Sensing Services Market

Historical CAGR of Remote Sensing System Market (2018 to 2022)	17.6%
Historical Value of Remote Sensing Systems Market (2022)	US$ 15.7 billion

Country-wise Insights

Country	Projected Value (2033)
United States	US$ 12.2 billion
United Kingdom	US$ 2.7 billion
China	US$ 10.4 billion
Japan	US$ 7.4 billion
South Korea	US$ 4.2 billion

Category-wise Insights

Category	Satellites
Value-based CAGR (2023 to 2033)	13.8%

Category	Spatial
Value-based CAGR (2023 to 2033)	13.6%

Scope of the Report

Attribute	Details
Estimated Market Size (2023)	US$ 18.4 billion
Projected Market Valuation (2033)	US$ 68.0 billion
Value-based CAGR (2023 to 2033)	14.0%
Forecast Period	2023 to 2033
Historical Data Available for	2018 to 2022
Market Analysis	Value (US$ billion/million) and Volume (MT)
Key Regions Covered	Latin America, North America, Europe, South Asia, East Asia, Oceania, and Middle East & Africa
Key Countries Covered	United States, Mexico, Brazil, Chile, Peru, Argentina, Germany, France, Italy, Spain, Canada, United Kingdom, Belgium, Nordic, Poland, Russia, Japan, South Korea, China, Netherlands, India, Thailand, Malaysia, Indonesia, Singapore, Australia, New Zealand, GCC Countries, South Africa, Central Africa, and others
Key Market Segments Covered	Platform Resolution End-use Region
Key Companies Profiled	Antrix Corporation Ltd Cyberswift LLC DigitalGlobe Ekofastba S.L Geo Sense Sdn. Bhd