What is SEPAL?

SEPAL (https://sepal.io/) is a free and open source cloud computing platform for geo-spatial data access and processing. It empowers users to quickly process large amounts of data on their computer or mobile device. Users can create custom analysis ready data using freely available satellite imagery, generate and improve land use maps, analyze time series, run change detection and perform accuracy assessment and area estimation, among many other functionalities in the platform. Data can be created and analyzed for any place on Earth using SEPAL.

https://data.apps.fao.org/catalog/dataset/9c4d7c45-7620-44c4-b653-fbe13eb34b65/resource/63a3efa0-08ab-4ad6-9d4a-96af7b6a99ec/download/cambodia_mosaic_2020.png" alt="alt text" title="Figure 1: Best pixel mosaic of Landsat 8 data for 2020 over Cambodia">

Figure 1: Best pixel mosaic of Landsat 8 data for 2020 over Cambodia

SEPAL reaches over 5000 users in 180 countries for the creation of custom data products from freely available satellite data. SEPAL was developed as a part of the Open Foris suite, a set of free and open source software platforms and tools that facilitate flexible and efficient data collection, analysis and reporting. SEPAL combines and integrates modern geospatial data infrastructures and supercomputing power available through Google Earth Engine and Amazon Web Services with powerful open-source data processing software, such as R, ORFEO, GDAL, Python and Jupiter Notebooks. Users can easily access the archive of satellite imagery from NASA, the European Space Agency (ESA) as well as high spatial and temporal resolution data from Planet Labs and turn such images into data that can be used for reporting and better decision making.

National Forest Monitoring Systems in many countries have been strengthened by SEPAL, which provides technical government staff with computing resources and cutting edge technology to accurately map and monitor their forests. The platform was originally developed for monitoring forest carbon stock and stock changes for reducing emissions from deforestation and forest degradation (REDD+). The application of the tools on the platform now reach far beyond forest monitoring by providing different stakeholders access to cloud based image processing tools, remote sensing and machine learning for any application. Presently, users work on SEPAL for various applications related to land monitoring, land cover/use, land productivity, ecological zoning, ecosystem restoration monitoring, forest monitoring, near real time alerts for forest disturbances and fire, flood mapping, mapping impact of disasters, peatland rewetting status, and many others.

The Hand-in-Hand initiative enables countries that generate data through SEPAL to disseminate their data widely through the platform and to combine their data with the numerous other datasets available through Hand-in-Hand.

https://data.apps.fao.org/catalog/dataset/9c4d7c45-7620-44c4-b653-fbe13eb34b65/resource/868e59da-47b9-4736-93a9-f8d83f5731aa/download/probability_classification_over_zambia.png" alt="alt text" title="Figure 2: Image classification module for land monitoring and mapping. Probability classification over Zambia">

Figure 2: Image classification module for land monitoring and mapping. Probability classification over Zambia

Snapshot img

The mobile map market share is expected to increase by USD 6.73 billion from 2020 to 2025, and the market’s growth momentum will accelerate at a CAGR of 18.41%. This mobile map market research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. The mobile map market report also offers information on several market vendors, including Alibaba Group Holding Ltd., Alphabet Inc., Apple Inc., CE Info. Pvt. Ltd., Environmental Systems Research Institute Inc., HERE Global BV, Microsoft Corp., NavInfo Co. Ltd., TomTom International BV, and Verizon Communications Inc. among others. Furthermore, this report extensively covers mobile map market segmentation by application (outdoor mobile map and indoor mobile map), end-user (automotive navigation, mobile and internet, and public sector and enterprise), and geography (APAC, North America, Europe, South America, and MEA).

What will the Mobile Map Market Size be During the Forecast Period?

Download the Free Report Sample to Unlock the Mobile Map Market Size for the Forecast Period and Other Important Statistics

Mobile Map Market: Key Drivers, Trends, and Challenges

Based on our research output, there has been a positive impact on the market growth during and post COVID-19 era. The increasing adoption of technologically advanced mobile devices is notably driving the mobile map market growth, although factors such as threat from open-source platform may impede market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the mobile map industry. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key Mobile Map Market Driver

The increasing adoption of technologically advanced mobile devices is one of the primary factors driving the mobile map market growth. The growing penetration of advanced mobile devices has increased the use of location-based services (LBS). To support this, mobile device manufacturers are introducing new devices that can integrate location-based applications such as GPS-enabled applications. In addition, individuals rely on such devices to obtain information such as traffic updates, directions to nearby locations, and real-time information such as weather forecasts. All these GPS-based applications are built on digital maps. Furthermore, the growth in connected devices will drive the demand for mobile maps globally to enable seamless navigation.

Key Mobile Map Market Trend

The development of indigenous mapping systems is one of the major mobile map market trends. This trend is growing significantly in Brazil, Russia, China, and India. Governments are encouraging regional mobile map makers to develop mobile map solutions that are country-specific. This trend is further supported by advanced mapping technology, which can develop accurate 3D digital maps.

Key Mobile Map Market Challenge

The growing popularity of open-source solutions has an adverse effect on the net sales of commercial mobile map solutions. The inflated cost of mobile map solutions has increased the demand for open-source mobile map applications in the market, especially in emerging countries such as China and India. These nations consist of many SMEs that require mobile map solutions but do not have sufficient capital to invest in customized mobile map technology. Therefore, open-source mobile map solutions have become a preferred choice among them. Many automobile companies also prefer open-source mobile map solutions in their vehicles. Large companies, as a part of the cost reduction, now prefer using open-source mobile map solutions compared with commercial mobile map solutions.

This mobile map market analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth. The actionable insights on the trends and challenges will help companies evaluate and develop growth strategies for 2021-2025.

Who are the Major Mobile Map Market Vendors?

The report analyzes the market’s competitive landscape and offers information on several market vendors, including:

Alibaba Group Holding Ltd.
Alphabet Inc.
Apple Inc.
CE Info. Pvt. Ltd.
Environmental Systems Research Institute Inc.
HERE Global BV
Microsoft Corp.
NavInfo Co. Ltd.
TomTom International BV
Verizon Communications Inc.

This statistical study of the mobile map market encompasses successful business strategies deployed by the key vendors. The mobile mapping market is fragmented and the vendors are deploying growth strategies such as M&A activities to compete in the market.

To make the most of the opportunities and recover from post COVID-19 impact, market vendors should focus more on the growth prospects in

UNICEF is providing technical assistance in ICT4D to implement the learner tracking component of the BTS/SIS strategy, using Ona Data and ODK Collect. Ona Data allows setting up a mobile data collection platform for conducting field surveys capturing photos and GPS points. ODK Collect is an open source Android app that replaces paper forms used in survey-based data collection.

The availability of low-cost biometric hardware sensors and software makes it possible to rapidly, affordably and securely sample and store a unique and invariant biological signature (or biometric “template”) for the purposes of identification. This has applications in research and trials, particularly for purposes of consent, linkage of case reporting forms collected at different times, and in the confirmation of participant identity for purposes of safety monitoring and adherence to international data laws. More broadly, these methods are applicable to the needs of the billion people who live in resource-restricted settings without identification credentials. The use of mobile electronic data collection software has recently become commonplace in clinical trials, research and actions for public good. A raft of tools based on the open-source ODK project now provide diverse options for data management that work consistently in resource-restricted settings, but none have built-in functionality for capturing biometric templates. In this study, we report the development and validation of a novel open-source app and associated method for capturing and matching biometric fingerprint templates during data collection with the popular data platforms ODK, KoBoToolbox, SurveyCTO, Ona and CommCare. Using data from more than 1,000 fingers, we show that fingerprint templates can be used to link data records with high accuracy. The accuracy of this process increases through the linkage of multiple fingerprints to each data record. By focussing on publishing open-source code and documentation, and by using an affordable (

For the protection of people and society against harm and health threats -- especially for the COVID-19 pandemic -- a variety of different disciplines needs to be involved. The data collection of very basic and health-related data of individuals in today's highly mobile society does help to plan, protect, and identify next steps health authorities and governments can, shall, or need to plan for or even implement. Thus, every individual, every human, and every inhabitant of the world is the key player -- very different to many past crises'. And since the individual is involved -- all individuals -- his/her (a) health and (b) privacy shall be considered in a very carefully crafted balance, not overruling one aspect with another one or even prioritizing certain aspects. Privacy remains the key. Thus, the solution of the current pandemic's data collection can be based on a fully privacy-preserving application, which can be used by individuals on their mobile devices, such as smartphones, while maintaining at the same time their privacy. Additionally, respective data collected in such a fully distributed setting does help to confine the pandemic and can be achieved in a democratic and very open, but still and especially privacy-protecting world. Therefore, the WeTrace approach and application as described in this paper utilizes the Bluetooth Low Energy (BTE) communication channel, many modern mobile devices offer, where asymmetric cryptography is being applied to allows for the decyphering of a message for that destination it had been intended for. Since literally every other potential participant only listens to random data, even a brute force attack will not succeed. WeTrace and its Open Source implementation is the only known approach so far, which ensures that any receiver of a message knows that this is for him/her, but does not know who the original sender was.

Abstract

This study aims to investigate the impacts of offering this additional year of pre-primary education in Bangladesh on child development outcomes (cognitive and social emotional), and will examine the benefits relative to the costs of the program. The study will also examine the mechanisms through which EYPP affects the outcomes of interest (e.g., children's school readiness), and the operational and community conditions for program implementation. This study will provide evidence for the Government of Bangladesh on how and how much the additional year of preschool benefits children, and at what cost. In addition to informing future policy in Bangladesh, this information may also be useful for other countries considering similar programming.

Geographic coverage

Three Upazilas - Gangni, Meherpur Sadar, and Mujibnagar under Meherpur District

Sampling procedure

All children that participated in the Baseline survey and their respective households were selected as the sample frame for the Midline survey. Table 1 of the survey report presents the targeted coverage by upazila and intervention area. In addition, 50 treatment school (Rangdhuno) teachers were also selected for interview using a structured questionnaire. To gain further insights on the pre-school performance and school community, four Focus Group Discussions (FGD) with School Management Committee (SMC) were conducted in Meherpur Sadar. Of the 1,856 children recruited for this study, 908 were girls and 948 were boys.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Alike the baseline survey, International Development and Early Learning Assessment (IDELA) tools is used for preschool children development assessment. The instrument used for collection of household information was revised and a new instrument to conduct Early Year Pre-School Program (EYPP) school teacher interview was introduced. Customized software was developed for the IDELA test; for the household data collection using Open Data Kit (ODK) tool. ODK is a free and open-source set of tools to manage mobile data collection solutions. Developing the IDELA tools under ODK was not difficult, involving minor modifications to an earlier version. Apps for household data collection was developed by DI.

Training on collecting data using mobile devices on Kobo Toolbox, an open-source data collection application. This training was developed by UNEP Sudan and can be given in person with practical exercises or used for self-training purposes.

Through the GeoNutrition project, 1352 georeferenced staple cereal grain and soil sample pairs from Ethiopia’s Amhara, Oromia, and Tigray regions and 1812 sample pairs from Malawi were collected from farms under a rainfed and smallholder farming systems. Field metadata was collected using KoBoCollect, an open-source georeferenced mobile data collection application. The elemental concentration in the staple cereals and soil properties were determined using standard wet chemistry digestion, extraction, and analysis procedures. A total of 29 cereal grain elemental concentrations from Ethiopia and Malawi, and 84 and 69 soil elemental concentrations or fractions from Ethiopia and Malawi, respectively were analysed and reported in these datasets. The major staple cereal crops sampled from Ethiopia included barley (n=175), finger millet (n=37), maize (n=290), sorghum (n=135), Teff (n=362), and wheat (n=325). Staple cereals collected from Malawi included maize (n=1608), pearl millet (n=32), rice (n=54) and sorghum (n=117). The datasets are presented in a workbook or zipped comma separated value (CSV) file for Ethiopia and Malawi. These datasets may be of interest to researchers, public institutions and non-governmental organisations working in the fields of agriculture, public health and nutrition, environmental protection, and fertiliser development.

Problem: Often spreadsheets are used as pseudo-databases for the storage of plot-based survey data, but they have major limitations in scalability, concurrent access and data retrieval. Paper-based surveys require time-consuming data entry. They contain potential inconsistencies (e.g. miss-spellings, abbreviations, missing values), particularly if coming from different observers due to unenforceable data standards.Methods: We analysed more than 30 years of data collected in the Northern Jarrah Forest (NJF) of south-western Australia, comprising c. 31,000 plots (c. 550,000 species records) and associated environmental variables stored across multiple spreadsheets in the development of our free and open source floristic information management system (FIMS). Data dictionaries were developed for each spreadsheet before being combined into a unified standard. OpenRefine software was used to ensure adherence to the standard, including correcting inconsistent field order in different files, removal of redundant or irrelevant fields, abolishing synonyms and abbreviations, and deleting incomplete rows. Database design and normalisation rules ensured the removal of repeating groups and the provision of fields for each retained attribute. Geometry was stored using spatial objects available in PostGIS whilst maintaining an otherwise relational database using PostgreSQL.Results: FIMS provides a spatial database system for storing, accessing and retrieving floristic survey data. FIMS includes a mobile data collection module for use on tablet technology with autonomous database synchronisation and one-step data entry to eliminate transcription and associated errors. Spatial data types enable the retrieval of data for viewing and analysis within most Geographic Information Systems and statistical software. It promotes portability and adaption to other locations and studies via the provision of all necessary code.

These are the design files of the manuscript entitled 'Phone Cam Array – an open-source, modular photogrammetry system made of Android phones'.

Data literacy is the ability to read, understand, work with, analyze, and argue with data. It is also the ability to derive meaningful information from data. Data literacy is not simply the ability to read text since it requires quantitative and analytical skills (for example: mathematical and statistical) involving reading and understanding data. Hence, with increased data literacy, one will be able to produce more insightful and evidence-based stories. This program has been localized to meet the local context of Thailand. EWMI-ODI and training team would like to express gratitude to the original program of World Bank’s Data Literacy Program, and advisors who supported the curriculum improvement for Thailand. This component introduces basic knowledge of data formats, the skills to find data online, sample tools that are used for collecting data, and the concepts to transform data into stories. Starting with a review of data formats, the unit moves on to techniques used to find, convert and process data that is in different formats, and how to develop a hypothesis and questions for a data story. Selected open-source tools will be introduced to you in case you require tools supporting your data collection, such as Mapeo - Mobile (only available in Android at the moment).

It contains data sets from Weber et al, Journal of Cell Science, 2024 (https://doi.org/10.1242/jcs.262166) The details for citation provided in the README file. Please cite this item as: Florian Weber, Sofiia Iskrak, Franziska Ragaller, Jan Schlegel, Birgit Plochberger, Erdinc Sezgin, Luca A. Andronico DOI: 10.17044/scilifelab.26233184 It contains microscopy images and excel sheets of the data. Abstract: Environment-sensitive probes are frequently used in spectral/multi-channel microscopy to study alterations in cell homeostasis. However, the few open-source packages available for processing of spectral images are limited in scope. Here, we present VISION, a stand-alone software based on Python for spectral analysis with improved applicability. In addition to classical intensity-based analysis, our software can batch-process multidimensional images with an advanced single-cell segmentation capability and apply user-defined mathematical operations on spectra to calculate biophysical and metabolic parameters of single cells. VISION allows for 3D and temporal mapping of properties such as membrane fluidity and mitochondrial potential. We demonstrate the broad applicability of VISION by applying it to study the effect of various drugs on cellular biophysical properties; the correlation between membrane fluidity and mitochondrial potential; protein distribution in cell-cell contacts; and properties of nanodomains in cell-derived vesicles. Together with the code, we provide a graphical user interface for facile adoption. Data usage Researchers are welcome to use the data contained in the dataset for any projects. Please cite this item upon use or when published. We encourage reuse using the same CC BY 4.0 License. Data Content lsm or czi files for confocal and spectral images tif files for super-resolution images Excel files for graphs

Software to open files .xlsx - Microsoft Excel .tif, .lsm, .czi - Fiji (https://imagej.net/software/fiji/)

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

National coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Algeria is 1002.

Mode of data collection

Mobile telephone

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

Snapshot img

GIS In Utility Industry Market Size 2025-2029

The gis in utility industry market size is forecast to increase by USD 3.55 billion, at a CAGR of 19.8% between 2024 and 2029.

The utility industry's growing adoption of Geographic Information Systems (GIS) is driven by the increasing need for efficient and effective infrastructure management. GIS solutions enable utility companies to visualize, analyze, and manage their assets and networks more effectively, leading to improved operational efficiency and customer service. A notable trend in this market is the expanding application of GIS for water management, as utilities seek to optimize water distribution and reduce non-revenue water losses. However, the utility GIS market faces challenges from open-source GIS software, which can offer cost-effective alternatives to proprietary solutions. These open-source options may limit the functionality and support available to users, necessitating careful consideration when choosing a GIS solution. To capitalize on market opportunities and navigate these challenges, utility companies must assess their specific needs and evaluate the trade-offs between cost, functionality, and support when selecting a GIS provider. Effective strategic planning and operational execution will be crucial for success in this dynamic market.

What will be the Size of the GIS In Utility Industry Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe Global Utilities Industry Market for Geographic Information Systems (GIS) continues to evolve, driven by the increasing demand for advanced data management and analysis solutions. GIS services play a crucial role in utility infrastructure management, enabling asset management, data integration, project management, demand forecasting, data modeling, data analytics, grid modernization, data security, field data capture, outage management, and spatial analysis. These applications are not static but rather continuously unfolding, with new patterns emerging in areas such as energy efficiency, smart grid technologies, renewable energy integration, network optimization, and transmission lines. Spatial statistics, data privacy, geospatial databases, and remote sensing are integral components of this evolving landscape, ensuring the effective management of utility infrastructure. Moreover, the adoption of mobile GIS, infrastructure planning, customer service, asset lifecycle management, metering systems, regulatory compliance, GIS data management, route planning, environmental impact assessment, mapping software, GIS consulting, GIS training, smart metering, workforce management, location intelligence, aerial imagery, construction management, data visualization, operations and maintenance, GIS implementation, and IoT sensors is transforming the industry. The integration of these technologies and services facilitates efficient utility infrastructure management, enhancing network performance, improving customer service, and ensuring regulatory compliance. The ongoing evolution of the utilities industry market for GIS reflects the dynamic nature of the sector, with continuous innovation and adaptation to meet the changing needs of utility providers and consumers.

How is this GIS In Utility Industry Industry segmented?

The gis in utility industry industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ProductSoftwareDataServicesDeploymentOn-premisesCloudGeographyNorth AmericaUSCanadaEuropeFranceGermanyRussiaMiddle East and AfricaUAEAPACChinaIndiaJapanSouth AmericaBrazilRest of World (ROW).

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.In the utility industry, Geographic Information Systems (GIS) play a pivotal role in optimizing operations and managing infrastructure. Utilities, including electricity, gas, water, and telecommunications providers, utilize GIS software for asset management, infrastructure planning, network performance monitoring, and informed decision-making. The GIS software segment in the utility industry encompasses various solutions, starting with fundamental GIS software that manages and analyzes geographical data. Additionally, utility companies leverage specialized software for field data collection, energy efficiency, smart grid technologies, distribution grid design, renewable energy integration, network optimization, transmission lines, spatial statistics, data privacy, geospatial databases, GIS services, project management, demand forecasting, data modeling, data analytics, grid modernization, data security, field data capture, outage ma

The global open source media players market is projected to reach a value of XXX million by 2033, expanding at a CAGR of XX% during the forecast period (2025-2033). The growing popularity of open source software, the increasing adoption of mobile devices, and the rising demand for high-quality multimedia content are driving the growth of this market. Key trends shaping the market include the adoption of cloud-based media players, the development of new and innovative media formats, and the increasing popularity of subscription-based streaming services. The market is segmented by application (mobile, computer, others), type (Windows, Linux, Mac), and region (North America, South America, Europe, Middle East & Africa, Asia Pacific). Key players in the market include VLC, Miro, MPV, Banshee, Audacious, Apprentic, Ffmpeg, SMPlayer, Xine, Deepin Movie, ExMplayer, CMPlayer, Clementine, Rhythmbox, and Spotify. The market in Asia Pacific is expected to witness the highest growth during the forecast period, driven by the increasing adoption of open source software in the region.

Snapshot img

GIS Market Size 2025-2029

The GIS market size is forecast to increase by USD 24.07 billion, at a CAGR of 20.3% between 2024 and 2029.

The Global Geographic Information System (GIS) market is experiencing significant growth, driven by the increasing integration of Building Information Modeling (BIM) and GIS technologies. This convergence enables more effective spatial analysis and decision-making in various industries, particularly in soil and water management. However, the market faces challenges, including the lack of comprehensive planning and preparation leading to implementation failures of GIS solutions. Companies must address these challenges by investing in thorough project planning and collaboration between GIS and BIM teams to ensure successful implementation and maximize the potential benefits of these advanced technologies.
By focusing on strategic planning and effective implementation, organizations can capitalize on the opportunities presented by the growing adoption of GIS and BIM technologies, ultimately driving operational efficiency and innovation.

What will be the Size of the GIS Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free Sample

The global Geographic Information Systems (GIS) market continues to evolve, driven by the increasing demand for advanced spatial data analysis and management solutions. GIS technology is finding applications across various sectors, including natural resource management, urban planning, and infrastructure management. The integration of Bing Maps, terrain analysis, vector data, Lidar data, and Geographic Information Systems enables precise spatial data analysis and modeling. Hydrological modeling, spatial statistics, spatial indexing, and route optimization are essential components of GIS, providing valuable insights for sectors such as public safety, transportation planning, and precision agriculture. Location-based services and data visualization further enhance the utility of GIS, enabling real-time mapping and spatial analysis.

The ongoing development of OGC standards, spatial data infrastructure, and mapping APIs continues to expand the capabilities of GIS, making it an indispensable tool for managing and analyzing geospatial data. The continuous unfolding of market activities and evolving patterns in the market reflect the dynamic nature of this technology and its applications.

How is this GIS Industry segmented?

The GIS industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Software
  Data
  Services


Type

  Telematics and navigation
  Mapping
  Surveying
  Location-based services


Device

  Desktop
  Mobile


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    China
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.

The Global Geographic Information System (GIS) market encompasses a range of applications and technologies, including raster data, urban planning, geospatial data, geocoding APIs, GIS services, routing APIs, aerial photography, satellite imagery, GIS software, geospatial analytics, public safety, field data collection, transportation planning, precision agriculture, OGC standards, location intelligence, remote sensing, asset management, network analysis, spatial analysis, infrastructure management, spatial data standards, disaster management, environmental monitoring, spatial modeling, coordinate systems, spatial overlay, real-time mapping, mapping APIs, spatial join, mapping applications, smart cities, spatial data infrastructure, map projections, spatial databases, natural resource management, Bing Maps, terrain analysis, vector data, Lidar data, and geographic information systems.

The software segment includes desktop, mobile, cloud, and server solutions. Open-source GIS software, with its industry-specific offerings, poses a challenge to the market, while the adoption of cloud-based GIS software represents an emerging trend. However, the lack of standardization and interoperability issues hinder the widespread adoption of cloud-based solutions. Applications in sectors like public safety, transportation planning, and precision agriculture are driving market growth. Additionally, advancements in technologies like remote sensing, spatial modeling, and real-time mapping are expanding the market's scope.

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The Software segment was valued at USD 5.06 billion in 2019

Emission data from UAV flights. This dataset is associated with the following publication: Zhou, X., J. Aurell, B. Mitchell, D. Tabor, and B. Gullett. A small, lightweight multipollutant sensor system for ground-mobile and aerial emission sampling from open area sources. JOURNAL OF AIR AND WASTE MANAGEMENT. Air & Waste Management Association, Pittsburgh, PA, USA, 154: 31-41, (2017).

# Motion Sensor-Based Mobile Device Fingerprinting (MSMDF)

This database was collected by Carlos **Sulbaran Fandino** under the supervision of **Anne Josiane Kouam** and **Konrad Rieck**.

The database is divided in 2 directories: *raw data*, and *fingerprints*. Along with it the *figures* directory provides different visualizations of the different fingerprinting-datasets.

## Raw data
This repository contains the sensor data collected for our experiments. The repository is divided in 4 sub-repositories:
 1. **Example recordings:** This directory contains 1 recording representing a full data collection session and 2 motivational recordings representing two devices placed side by side on a desk. 
 2. **Original recordings:** This repository contains *340 two-minutes* recordings each named *"Device ID - Recording Instance"*. Each recording instance contains a *Metadata.csv* file (device name, platform, device id) together with 4 csv files corresponding to the different sensors: *Accelerometer.csv* , *Gravity.csv* , *Gyroscope.csv* , *Orientation.csv*. 
 3. **Separated by setting:** This repository contains 6 sub-repositories each corresponding to a different data collection setting (environmental condition). The sensor data in this directory is the result of the pre-processing stage of our MSMDF evaluation, therefore additional data streams have been added to each csv file and the *Orientation.csv* file is not included. 
 4. **Protected data - reduced:** This repository contains a reduced version of *separated by setting*, for each of the parameters used to evaluate the countermeasures. To produce a full version use: [CountermeasureApplier.py](https://github.com/carlossulba/MSMDF-Study/blob/main/Code/CountermeasureApplier.py) 


Each recording corresponds to a data collection session where the user: **1.** Holds its phone in hand for 10 seconds, **2.** Places it on a desk for 10 seconds, **3.** Holds it again in hand for 10 seconds but with inaudible audio stimulation, **4.** Again places the phone on a desk but with inaudible audio stimulation, **5.** Holds the phone on hand with extended arm while taking 10 steps in a straight line, and finally **6.** Repeats step five.


## Fingerprints
This repository contains a fingerprint-dataset for each fingerprint design. For each fingerprint design parameter you can find a respective sub-directory.

The following directories contain the fingerprint-datasets for each design parameter: 
 1. Sensor selection
 2. DC conditions
 3. Data stream set
 4. Feature set
 5. Window length (s)
 6. Sampling rate (Hz)
 7. Default
 8. Min FPs per device 

Inside them you will find 1 pickle file (.pkl) for each fingerprint design.

The following directories contain the fingerprint-dataset for each countermeasure parameter:
 1. Countermeasure strength
 2. Countermeasure resampling frequency

These were extracted after applying an anonimization step to the raw sensor data before extracting the fingerprints with the default fingerprint design.



## Using a fingerprint-dataset
The following data-structure describes the fingerprint-datasets. You can use them for training your own models or evaluating their distribution in the space.
```python
{
  'fingerprints': dict
  'config': FingerprintConfig
}
```

For opening a fingerprint-dataset you can use the following example code: [open fingerprint.py]()

The following data-structure describes the *fingerprints* dictionary.
```python
{
  'Setting 1': {
    'Device-01': [
      Fingerprint_01,
      Fingerprint_02
      ],
    'Device-02': [
      Fingerprint_01,
      Fingerprint_02,
      ],
  },
  'Setting 2': {
    'Device-01': [
      Fingerprint_01,
      Fingerprint_02,
      ],
  }
}
```

The following data-structure describes the *FingerprintConfig*.
```python
{
  "data_location": string,
  "fingerprint_length": int,
  "sampling_rate": int,
  "enabled_settings": list,
  "enabled_sensors": list,
  "enabled_streams": list,
  "enabled_features": list,
  "min_recordings": int,
  "repositioning": bool,
  "spectral_brightness_threshold": int,
  "spectral_rolloff_threshold": float,
  "frame_duration": float
}
```

# License
This database is open-source and available under the [GNU General Public License (GPL)](https://www.gnu.org/licenses/gpl-3.0.en.html). By using this database, you agree to the following conditions:
- Use responsibly and ethically.
- Cite this repository in your work or research.
- Ensure that any derivative works or modifications are open-sourced under the same license.

The SMART Infrastructure Facility, as part of the PetaJakarta.org project has developed a spatio-topological model of the hydrological infrastructure network for Jakarta, Indonesia. The Petajakarta.org hydrological infrastructure network is a spatial-topological model which describes how floodgates, pumps and waterways are both spatially and topological connected within the city. Using a graph theory approach pumps and floodgates are represented as network nodes, and waterways (rivers, canals, and streams) are represented as edges. Topology is encoded within the data using a system of unique node and edge primary keys. The model is a directed graph consisting of 550 nodes and 618 edges. Directionality was inferred by edge orientation, assuming the general condition of water flowing from south Jakarta to the north. The model is based on waterways, and pumps & floodgates data also available through the PetaJakarta.org Major Open Data Collection.

Network model produced January 2015. Source data captured 2013.

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