An integrated open mineral spectral library designed to enhance the utility and precision of mineral spectral data for geothermal exploration, developed from a reliable and comprehensive digital dataset for seamless sharing by integrating field data, the USGS spectral library, and pertinent information obtained from Coso geothermal field (Coso) in California. The ASD FieldSpec portable spectrometer was utilized for collecting spectral data, which was subsequently analyzed using the THOR Material Identification tool in ENVI, The Spectral Geologist (TSG) software by CSIRO, and the Fully Constrained Linear Spectral Unmixing algorithm (FCLSU) in MATLAB. Scanning Electron Microscopy (SEM) with a mineralogy-analyzing function was employed to assess the mineral composition of samples, ensuring precise mineralogical analysis. A portable X-ray fluorescence (pXRF) spectrometer was also utilized to gather information on elemental enrichment. A framework for developing spectra data and establishing spectral libraries for various geological cases was proposed within this study. The characteristic spectra of six alteration minerals - alunite, chalcedony, epidote, hematite, kaolinite, and opal - were acquired from Coso samples. The spectral library for the Coso alteration minerals was introduced for further application in academic study or industrial exploration. To browse the Coso Geothermal Spectral data and related figures from spreadsheets: 1 Unzip and store the following items in the same folder. 'Contact Probe Data.zip', 'Sample Photos.zip', and 'Coso spectra of higher-certainity minerals.xlsx'. 2 Open 'Coso spectra of higher-certainity minerals.xlsx'. The hyperlinks in the spreadsheet lead to the folders or figures of: spectra .asd file, spectra ASC II file, spectra plots, and sample photos. The spectra data is raw data without splice correction. Spectra .asd files require particular software to open. (These cannot be opened in GIS software such as ArcGIS.) Spectra ASC II files can be opened in a text editor or spread sheet program.

The 4 global maps of pyroxene-related spectral parameters derived from data coming from the VIR mapping spectrometer onboard NASA/Dawn acqusition campaing at Vesta.

This data set contains reduced-resolution QuickBird imagery and geospatial data for the entire Barrow QuickBird image area 156.15° W - 157.07° W, 71.15° N - 71.41° N) and the Barrow B4 Quadrangle (156.29° W - 156.89° W, 71.25° N - 71.40° N), for use in Geographic Information Systems (GIS) and remote sensing software. The original QuickBird data sets were acquired by DigitialGlobe from 1 to 2 August 2002, and consist of orthorectified satellite imagery. Federal Geographic Data Committee (FGDC)-compliant metadata for all value-added data sets are provided in text, HTML, and XML formats.

Accessory layers include: 1:250,000- and 1:63,360-scale USGS Digital Raster Graphic (DRG) mosaic images (GeoTIFF format); 1:250,000- and 1:63,360-scale USGS quadrangle index maps (ESRI Shapefile format); an index map for the 62 QuickBird tiles (ESRI Shapefile format); and a simple polygon layer of the extent of the Barrow QuickBird image area and the Barrow B4 quadrangle area (ESRI Shapefile format).

The baseline geospatial data support education, outreach, and multi-disciplinary research of environmental change in Barrow, which is an area of focused scientific interest.

Data are available either via FTP or on CD-ROM.

The spectrum of human impact.

1H NMR profiling is nowadays a consolidated technique for the identification of geographical origin of food samples. The common approach consists in correlating NMR spectra of food samples to their territorial origin by multivariate classification statistical algorithms. In the present work we illustrate an alternative perspective to exploit territorial information, contained in the NMR spectra, which is based on the implementation of a Geographic Information System (GIS). NMR spectra are used to build a GIS map permitting the identification of territorial regions having strong similarities in the chemical content of the produced food (terroir units). These terroir units can, in turn, be used as input for labeling samples to be analyzed by traditional classification methods. In this work we describe the methods and the algorithms which permits to produce GIS maps from NMR profiles and apply the described method to the analysis of the geographical distribution of olive oils in an Italian region. In particular, we analyzed by 1H NMR up to 98 georeferenced olive oil samples produced in the Abruzzo Italian region. By using the first principal component of the NMR variables selected according to the Moran test, we produced a GIS map, in which we identified two regions incidentally corresponding to the provinces of Teramo and Pescara. We then labeled the samples according to the province of provenience and built a LDA model which provides a classification ability up to 99 % . A comparison between the variables selected in the geostatistics and classification steps is finally performed.

Description Cellphone tower extract is a copy of the data found at https://ised-isde.canada.ca/site/spectrum-management-system/en/spectrum-management-system-data. Data is organized into a point layer of tower locations grouped by provider. All the providers transmitters are located in a table related by a unique TowerID.Dataset Usage General data layer for use when needed, for example to identify shortfalls in cell service for field work.Data Source Modified version of Innovation, Science and Economic Development Canada datasetData Criticality 1Sensitive Data NoCurator Greg SpiridonovCurator Job Title GIS SpecialistCurator Email greg.spiridonov@grey.caCurator Department IT / GISCurator Responsibilities Maintain,Oversight_Control_AccessMaintenance and Update Frequency MonthlyUpdate History New ZIP downloaded Nov 14 2023Published Map Service(s) https://gis.grey.ca/portal/home/item.html?id=718f08a731924856827f85178bb649cbPublicly Available Publicly availableOpen Data Published to Open DataOffline (sync) Not sure at this timeOther Comments Dataset relates to table GC_CellTower_TransmittersPermissionsAssign permissions to map service if published.Group PermissionsCurator Department ViewGrey County Staff ViewPublic View

According to many previous studies, application of remote sensing for the complex and heterogeneous urban environments in Sub-Saharan African countries is challenging due to the spectral confusion among features caused by diversity of construction materials. Resorting to classification based on spectral indices that are expected to better highlight features of interest and to be prone to unsupervised classification, this study aims (1) to evaluate the effectiveness of index-based classification for Land Use Land Cover (LULC) using an unsupervised machine learning algorithm Product Quantized K-means (PQk-means); and (2) to monitor the urban expansion of Luanda, the capital city of Angola in a Logistic Regression Model (LRM). Comparison with state-of-the-art algorithms shows that unsupervised classification by means of spectral indices is effective for the study area and can be used for further studies. The built-up area of Luanda has increased from 94.5 km2 in 2000 to 198.3 km2 in 2008 and to 468.4 km2 in 2018, mainly driven by the proximity to the already established residential areas and to the main roads as confirmed by the logistic regression analysis. The generated probability maps show high probability of urban growth in the areas where government had defined housing programs.

The area, proportion and vegetation area, proportion and coverage of the WLFZ classified by slope in each study area in 2019.

The Coronal Diagnostic Spectrometer is designed to probe the solar atmosphere through the detection of spectral emission lines in the extreme ultraviolet wavelength range 150 – 800 Å. By observing the intensities of selected lines and line profiles, temperature, density, flow and abundance information for the plasmas in the solar atmosphere can be derived. Spatial and temporal resolutions of down to a few arcseconds and seconds, respectively, allow such studies to be made within the finescale structure of the solar corona. Futhermore, coverage of large wavelength bands provides the capability for simultaneously observing the properties of plasmas across the wide temperature ranges of the solar atmosphere. The optical elements of CDS are composed of a grazing incidence WolterSchwartzhild type 2 telescope that feeds simultaneously two detectors, a Normal Incidence Spectrometer (NIS) and a Grazing Incidence Spectrometer (GIS), which share a common slit. In the NIS, the incoming beam is focused onto an 1024 x 512 pixels CCD detector, producing two spectrally dispersed stigmatic images. To build up larger images, a plane mirror, in front of the slit, is scanned through a small angle in 2 arcsecond steps. The NIS wavelength ranges are 308-381 Å (called NIS 1), 513-633 Å (NIS 2) with a time resolution down to 1 s or less. Its prime slits are 2 x 240, 4 x 240, 90 x 240 arcsec. The GIS comprises a spherical grating set a grazing incidence, with four microchannel plate detectors placed around the Rowland circle. The resulting spectra are astigmatic. Thus, for GIS operation, pin hole slits are used and, to enable images to be built up, the slits can be moved in one arcsecond increments in a plane perpendicular to the direction of dispersion, and the scan mirror moved in two arcsecond increments in the plane of dispersion. The GIS wavelength ranges are 151-221 (GIS 1), 256-338 (GIS 2), 393-493 (GIS 3), 656-785 (GIS 4), Å. Its prime slits are 2 x 2 arcsec (standard), 4 x 4 arcsec (alternative) or 8 x 51 arcsec (not normally used for science). Rasters are generated on CDS by moving the scan mirror between exposures for the NIS and by moving both the scan mirror and slit assembly for the GIS. The nominal steps of these are 2 and 1 arcsec respectively though the more precise values are 2.03 arsec and 1.01 arcsec. The maximum range in both axes is 240 arcsec. The field of view of the telescope is 4 arcminutes. The instrument can be repointed to cover the whole solar disc, as performed almost every month.

CDS scientific data products depends on the way CDS is operated. Various studies have been designed based on different scientific objectives including: Coronal Hole study, CME Onset Watch, Polarity Reversal study, Limb study... Each of such study is defined by where CDS is pointed at, the slit size, the raster used, the exposure time, the number of spectral lines or NIS bands used, the number of GIS bands used etc... Data products are provided in the form of a set of images (NIS) and/or spectra (NIS and GIS). The number of NIS images depends on the number of spectral lines or bands used while the size of the images depends on the slit size. Monthly Full Sun Disk images at six wavelengths are also available. Data products are available in the form of raw data, in Flexible Image Transport System (FITS) format, that can be calibrated through the IDL SolarSoft software. Calibrated images will eventually be provided.

Metadata: NOAA GOES-R Series Advanced Baseline Imager (ABI) Level 1b RadiancesMore information about this imagery can be found here.This satellite imagery combines data from the NOAA GOES East and West satellites and the JMA Himawari satellite, providing full coverage of weather events for most of the world, from the west coast of Africa west to the east coast of India. The tile service updates to the most recent image every 10 minutes at 1.5 km per pixel resolution.The infrared (IR) band detects radiation that is emitted by the Earth’s surface, atmosphere and clouds, in the “infrared window” portion of the spectrum. The radiation has a wavelength near 10.3 micrometers, and the term “window” means that it passes through the atmosphere with relatively little absorption by gases such as water vapor. It is useful for estimating the emitting temperature of the Earth’s surface and cloud tops. A major advantage of the IR band is that it can sense energy at night, so this imagery is available 24 hours a day.The Advanced Baseline Imager (ABI) instrument samples the radiance of the Earth in sixteen spectral bands using several arrays of detectors in the instrument’s focal plane. Single reflective band ABI Level 1b Radiance Products (channels 1 - 6 with approximate center wavelengths 0.47, 0.64, 0.865, 1.378, 1.61, 2.25 microns, respectively) are digital maps of outgoing radiance values at the top of the atmosphere for visible and near-infrared (IR) bands. Single emissive band ABI L1b Radiance Products (channels 7 - 16 with approximate center wavelengths 3.9, 6.185, 6.95, 7.34, 8.5, 9.61, 10.35, 11.2, 12.3, 13.3 microns, respectively) are digital maps of outgoing radiance values at the top of the atmosphere for IR bands. Detector samples are compressed, packetized and down-linked to the ground station as Level 0 data for conversion to calibrated, geo-located pixels (Level 1b Radiance data). The detector samples are decompressed, radiometrically corrected, navigated and resampled onto an invariant output grid, referred to as the ABI fixed grid.McIDAS merge technique and color mapping provided by the Cooperative Institute for Meteorological Satellite Studies (Space Science and Engineering Center, University of Wisconsin - Madison) using satellite data from SSEC Satellite Data Services and the McIDAS visualization software.

These raster and vector datasets were developed for the Sea Grant South Coast MPA Baseline Program as part of the project “Nearshore Substrate Mapping and Change Analysis using Historical and Concurrent Multispectral Imagery” (#R/MPA 30 10-049). The study region is the South Coast Region (SCR). Ocean Imaging acquired this imagery on 11/22/2011 using the Digital Multispectral Camera (DMSC). Details on this system and the data processing are below in the Lineage section of this document. Details on this system and the data processing are below in the Lineage section of this document. Individual DMSC image tiles were mosaicked into sections based on the islands covered and local coastal regions as well as the SCR MPA zones in order to generate this multispectral image product. These imagery were subsequently used to generate habitat classification thematic maps of the SCR's kelp beds from Point Conception to Imperial Beach, CA. The imagery files deliverd are in GeoTIFF format. This raster dataset contains a habitat classification of either offshore giant kelp beds along the California South Coast Region (SCR) from from Point Conception, CA down to Imperial beach, CA. This specific raster classification includes the South Point SMR and Skunk Point SMR. This dataset was originally uploaded to Oceanspaces (http://oceanspaces.org/) and the Knowledge Network for Biocomplexity (KNB, https://knb.ecoinformatics.org/data) in 2013 as part of the South Coast baseline monitoring program. In 2022 this dataset was moved to the California Ocean Protection Council Data Repository (https://opc.dataone.org/) by Mike Esgro (Michael.Esgro@resources.ca.gov) and Rani Gaddam (gaddam@ucsc.edu). At that time the GIS analysis products were added to the dataset. The long-term California MPA boundary and project info tables can be found as a separate dataset here: https://opc.dataone.org/view/doi:10.25494/P64S3W.

This product set contains high-resolution Interferometric Synthetic Aperture Radar (IFSAR) imagery and geospatial data for the Barrow Peninsula (155.39 - 157.48 deg W, 70.86 - 71.47 deg N) and Barrow Triangle (156.13 - 157.08 deg W, 71.14 - 71.42 deg N), for use in Geographic Information Systems (GIS) and remote sensing software. The primary IFSAR data sets were acquired by Intermap Technologies from 27 to 29 July 2002, and consist of Orthorectified Radar Imagery (ORRI), a Digital Surface Model (DSM), and a Digital Terrain Model (DTM).

Derived data layers include aspect, shaded relief, and slope-angle grids (floating-point binary and ArcInfo grid format), as well as a vector layer of contour lines (ESRI Shapefile format). Also available are accessory layers compiled from other sources: 1:250,000- and 1:63,360-scale USGS Digital Raster Graphic (DRG) mosaic images (GeoTIFF format); 1:250,000- and 1:63,360-scale USGS quadrangle index maps (ESRI Shapefile format); a quarter-quadrangle index map for the 26 IFSAR tiles (ESRI Shapefile format); and a simple polygon layer of the extent of the Barrow Peninsula (ESRI Shapefile format).

Unmodified IFSAR data comprise 26 data tiles across UTM zones 4 and 5. The DSM and DTM tiles (5 m resolution) are provided in floating-point binary format with header and projection files. The ORRI tiles (1.25 m resolution) are available in GeoTIFF format. FGDC-compliant metadata for all data sets are provided in text, HTML, and XML formats, along with the Intermap License Agreement and product handbook.

The baseline geospatial data support education, outreach, and multi-disciplinary research of environmental change in Barrow, which is an area of focused scientific interest.

Data are available through licensing only to National Science Foundation (NSF)-funded investigators. An NSF award number must be provided when ordering data. Contact NSIDC User Services at nsidc@nsidc.org to order the data, and include an NSF award number in the email.

This hosted feature layer is provided by the USDA FPAC-BC-GEO and shows image acquisition dates for 2022 National Agriculture Imagery Program (NAIP) imagery for Wisconsin. This date index is state based and contains a polygon for each exposure used in the creation of the imagery. Click on a polygon to find out more information about any area on the image. Attribute information includes the following: IDATE - Image acquisition date SDATE - Polygon start date/time - local 24 hour clock. The start/end time will be for the collection of the individual polygon (will be the same for frame based systems)EDATE - Polygon end date/time - local 24 hour clock. The start/end time will be for the collection of the individual polygon (will be the same for frame based systems)BCON - Color type - possible values are NC (natural color), CIR (color infrared), and M4B (4-band)CAM_TYPE - Camera type (Digital or film)CAM_MAN - Camera ManufacturerCAM_MOD - Camera modelHARD_FIRM - Camera HW and FW version which provides top level information specific to the camera systemSENSNUM - Sensor or lens serial numberAC_TYPE - Aircraft type - ICAO designation (i.e. C441 for a Cessna 441 Conquest II), airborne platforms only blank attribute for space-based systemsACTAILNUM - Aircraft tail number - airborne platforms only a blank attribute for space-based systemsSHAPE_AREA - Polygon area (square meters)RED_RNGE - Red electromagnetic spectrum - spectrum range in nano meters (604-664)GREEN_RNGE - Green electromagnetic spectrum - spectrum range in nano meters (533-587)BLUE_RNGE - Blue electromagnetic spectrum - spectrum range in nano meters (420-492)NIR_RNGE - Near infrared electromagnetic spectrum - spectrum range in nano meters (683-920)

The size of the Asia Pacific GIS market was valued at USD XXX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 9.08% during the forecast period.Geographic Information Systems are very powerful tools for capturing, storing, analyzing, and visualizing geographic data. The technology integrates maps with databases that assist organizations in understanding spatial relationships, patterns, and trends. Applications can be found across a broad spectrum of industries, such as urban planning, environmental management, agriculture, and public health.Asia Pacific is growing most rapidly in the regions relevant to the global market for Asia Pacific GIS. Growth is encouraged by factors like increasing levels of urbanization, increased infrastructures investments, and growth levels of awareness about GIS and what benefits it can offer to any organization. Lately, with the advancement of GIS technology like GIS solutions offered both on cloud and mobile environment has made access and usabilities much easier to the organizations.The applications of GIS in solving problems such as disaster management and climate change in the Asia Pacific region have become incredibly extensive. Examples of using GIS include mapping flood-prone areas, monitoring deforestation, and improving transportation networks. The greater the environmental and social challenge that faces this developing region, the more GIS is going to play a significant role in the discovery of meaningful insights for the guidance of informed decisions. Recent developments include: February 2024 - John Deere announced a strategic partnership with Hexagon’s Leica Geosystems to accelerate the digital transformation of the heavy construction industry. John Deere and Hexagon joined forces to bring cutting-edge technologies and solutions to construction professionals worldwide., January 2024 - BlackSky Technology Inc. won a first-in-class contract to support the Indonesian Ministry of Defence (MoD), supplying Gen-3 earth observation satellites, ground station capabilities, and flight operations support. BlackSky also won a multi-year contract to support the MoD in the supply of assured subscription-based real-time imagery (RTI) and analytics services. The multi-year contract was won by BlackSky Technology Inc. in partnership with Alenia Space, a subsidiary of Thales Group, to supply Assured subscription-based RTI and analytics services to the Indonesian Ministry of Defense. The total value of the two contracts is approximately USD 50 million.. Key drivers for this market are: Ease of Convenience of Shoppers Elevated Through No Traveling and Simpler Access Across Global Borders, Higher Return on Investment. Potential restraints include: Incidents of Fraudulent Transactions and Cyber Crime, Opening of Physical Spaces, Galleries, and Auctions Impacting Online Sales. Notable trends are: Cloud Deployment Segment to Hold Significant Market Share.

This hosted feature layer is provided by the USDA FPAC-BC-GEO and shows image acquisition dates for 2023 National Agriculture Imagery Program (NAIP) imagery for Arkansas.This date index is state based and contains a polygon for each exposure used in the creation of the imagery. Click on a polygon to find out more information about any area on the image. Attribute information includes the following:IDATE - Image acquisition dateSDATE - Polygon start date/time - local 24 hour clock. The start/end time will be for the collection of the individual polygon (will be the same for frame based systems)EDATE - Polygon end date/time -local 24 hour clock. The start/end time will be for the collection of the individual polygon (will be the same for frame based systems)BCON - Color type - possible values are NC (natural color), CIR (color infrared), and M4B (4-band)CAM_TYPE - Camera type (Digital or film)CAM_MAN - Camera ManufacturerCAM_MOD - Camera modelHARD_FIRM - Camera HW and FW version which provides top level information specific to the camera systemSENSNUM - Sensor or lens serial numberAC_TYPE - Aircraft type - ICAO designation (i.e. C441 for a Cessna 441 Conquest II), airborne platforms only blank attribute for space-based systemsACTAILNUM - Aircraft tail number - airborne platforms only a blank attribute for space-based systemsSHAPE_AREA - Polygon area (square meters)RED_RNGE - Red electromagnetic spectrum - spectrum range in nano meters (604-664)GREEN_RNGE - Green electromagnetic spectrum - spectrum range in nano meters (533-587)BLUE_RNGE - Blue electromagnetic spectrum - spectrum range in nano meters (420-492)NIR_RNGE - Near infrared electromagnetic spectrum - spectrum range in nano meters (683-920)

This product set contains reduced-resolution Interferometric Synthetic Aperture Radar (IFSAR) imagery and geospatial data for the Barrow Peninsula (155.39 - 157.48 deg W, 70.86 - 71.47 deg N), for use in Geographic Information Systems (GIS) and remote sensing software. The primary IFSAR data sets were acquired by Intermap Technologies from 27 to 29 July 2002, and consist of an Orthorectified Radar Imagery (ORRI), a Digital Surface Model (DSM), and a Digital Terrain Model (DTM).

Derived data layers include aspect, shaded relief, and slope-angle grids (floating-point binary format), as well as a vector layer of contour lines (ESRI Shapefile format). Also available are accessory layers compiled from other sources: 1:250,000- and 1:63,360-scale USGS Digital Raster Graphic (DRG) mosaic images (GeoTIFF format); 1:250,000- and 1:63,360-scale USGS quadrangle index maps (ESRI Shapefile format); and a simple polygon layer of the extent of the Barrow Peninsula (ESRI Shapefile format).

The DSM and DTM data sets (20 m resolution) are provided in floating-point binary format with header and projection files. The ORRI mosaic (5 m resolution) is available in GeoTIFF format. FGDC-compliant metadata for all data sets are provided in text, HTML, and XML formats, along with the Intermap License Agreement and product handbook. The baseline geospatial data support education, outreach, and multi-disciplinary research of environmental change in Barrow, which is an area of focused scientific interest. Data are available via FTP and CD-ROM.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

The global Geographic Information System (GIS) Software market is experiencing robust growth, driven by increasing adoption across various sectors, including government, utilities, and transportation. The market size in 2025 is estimated at $15 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This significant expansion is fueled by several key factors. The rising need for precise location-based data analysis, coupled with advancements in cloud computing and big data technologies, is enabling the development of sophisticated and scalable GIS solutions. Furthermore, the integration of GIS with other technologies, such as artificial intelligence (AI) and the Internet of Things (IoT), is opening new avenues for innovation and application. This leads to enhanced spatial data management, improved decision-making capabilities, and optimized resource allocation across diverse industries. Government initiatives promoting digital transformation and smart city development also contribute significantly to market growth. However, the market faces certain challenges. High initial investment costs for software and infrastructure, along with the need for skilled professionals to operate and maintain these systems, can hinder wider adoption, particularly among smaller organizations. Data security and privacy concerns associated with handling sensitive geospatial data also pose a significant restraint. Despite these limitations, the overall market outlook for GIS software remains highly positive, driven by the increasing reliance on location intelligence across a broad spectrum of industries and the continuous evolution of GIS technologies. The increasing availability of open-source GIS software is also expected to foster market growth, particularly in developing economies. By 2033, the market is projected to reach approximately $45 billion, signifying a substantial increase in market value and adoption.

Hydrologic data, primarily precipitation and runoff, have been collected on experimental watersheds operated by the U.S. Department of Agriculture Agricultural Research Service (USDA-ARS) and on other lands in southeastern Arizona since the 1950s. These data are of national and international importance and make up one of the most comprehensive semiarid watershed data sets in the world. The USDA-ARS Southwest Watershed Research Center has recently developed an electronic data processing system that includes an online interface (https://tucson.ars.ag.gov/dap) to provide public access to the data. The goal of the system is to promote analyses and interpretations of historic and current data by improving data access. The publicly accessible part of the system consists of an interactive Web site, which provides an interface to the data, and a relational database, which is used to process, store, and manage data. In addition, DAP was expanded to put sediment, meteorological, soil moisture and temperature, vegetation, CO2 and water flux, geographic information system (GIS) and aircraft and satellite spectral imagery data on line and to publish metadata for all WGEW long-term measurements. Resources in this dataset:Resource Title: Web Page. File Name: WGEWsoils.xls, url: https://www.tucson.ars.ag.gov/dap/Files/WGEWsoils.xls

The global Geographic Information System (GIS) Solutions market is experiencing robust growth, driven by increasing adoption across diverse sectors like transportation, architecture, engineering, and construction (AEC), telecommunications, and agriculture. The market's expansion is fueled by the need for efficient spatial data management, improved decision-making capabilities, and the rising demand for location-based services. Technological advancements, such as the integration of cloud computing, AI, and IoT, are further accelerating market growth. While precise figures for market size and CAGR were not provided, based on industry reports and the listed companies’ activities, a reasonable estimate would place the 2025 market size at approximately $15 billion, with a projected Compound Annual Growth Rate (CAGR) of 8-10% over the forecast period (2025-2033). This growth reflects the continuous integration of GIS into various applications, including smart city initiatives, precision farming, and disaster management. Despite the optimistic outlook, challenges remain. High initial investment costs for software and hardware, along with the need for skilled professionals to manage and analyze complex spatial data, can act as restraints. Data security and privacy concerns, coupled with the complexity of integrating GIS solutions with existing infrastructure, also pose hurdles for market expansion. However, the continuous development of user-friendly software, affordable cloud-based solutions, and the rising availability of skilled professionals are mitigating these challenges and supporting sustained growth in the market. The segmentation of the market into software, services, and applications across different sectors highlights the multifaceted nature of the GIS solution landscape, indicating diverse growth opportunities across a broad spectrum of industries.