Tree diameter and coordinate data obtained by iPhone, iPad, and conventional survey methods in a 1 ha forest plot in Hokkaido, Japan (42°59'57" N, 141°23'29" E). Tatsumi, Yamaguchi, Furuya (in press) ForestScanner: A mobile application for measuring and mapping trees with LiDAR-equipped iPhone and iPad. Methods in Ecology and Evolution.

The Robot-at-Home dataset (Robot@Home, paper here) is a collection of raw and processed data from five domestic settings compiled by a mobile robot equipped with 4 RGB-D cameras and a 2D laser scanner. Its main purpose is to serve as a testbed for semantic mapping algorithms through the categorization of objects and/or rooms.

This dataset is unique in three aspects:

The provided data were captured with a rig of 4 RGB-D sensors with an overall field of view of 180°H. and 58°V., and with a 2D laser scanner.

It comprises diverse and numerous data: sequences of RGB-D images and laser scans from the rooms of five apartments (87,000+ observations were collected), topological information about the connectivity of these rooms, and 3D reconstructions and 2D geometric maps of the visited rooms.

The provided ground truth is dense, including per-point annotations of the categories of the objects and rooms appearing in the reconstructed scenarios, and per-pixel annotations of each RGB-D image within the recorded sequences

During the data collection, a total of 36 rooms were completely inspected, so the dataset is rich in contextual information of objects and rooms. This is a valuable feature, missing in most of the state-of-the-art datasets, which can be exploited by, for instance, semantic mapping systems that leverage relationships like pillows are usually on beds or ovens are not in bathrooms.

Robot@Home2

Robot@Home2, is an enhanced version aimed at improving usability and functionality for developing and testing mobile robotics and computer vision algorithms. It consists of three main components. Firstly, a relational database that states the contextual information and data links, compatible with Standard Query Language. Secondly,a Python package for managing the database, including downloading, querying, and interfacing functions. Finally, learning resources in the form of Jupyter notebooks, runnable locally or on the Google Colab platform, enabling users to explore the dataset without local installations. These freely available tools are expected to enhance the ease of exploiting the Robot@Home dataset and accelerate research in computer vision and robotics.

If you use Robot@Home2, please cite the following paper:

Gregorio Ambrosio-Cestero, Jose-Raul Ruiz-Sarmiento, Javier Gonzalez-Jimenez, The Robot@Home2 dataset: A new release with improved usability tools, in SoftwareX, Volume 23, 2023, 101490, ISSN 2352-7110, https://doi.org/10.1016/j.softx.2023.101490.

@article{ambrosio2023robotathome2,title = {The Robot@Home2 dataset: A new release with improved usability tools},author = {Gregorio Ambrosio-Cestero and Jose-Raul Ruiz-Sarmiento and Javier Gonzalez-Jimenez},journal = {SoftwareX},volume = {23},pages = {101490},year = {2023},issn = {2352-7110},doi = {https://doi.org/10.1016/j.softx.2023.101490},url = {https://www.sciencedirect.com/science/article/pii/S2352711023001863},keywords = {Dataset, Mobile robotics, Relational database, Python, Jupyter, Google Colab}}

Version historyv1.0.1 Fixed minor bugs.v1.0.2 Fixed some inconsistencies in some directory names. Fixes were necessary to automate the generation of the next version.v2.0.0 SQL based dataset. Robot@Home v1.0.2 has been packed into a sqlite database along with RGB-D and scene files which have been assembled into a hierarchical structured directory free of redundancies. Path tables are also provided to reference files in both v1.0.2 and v2.0.0 directory hierarchies. This version has been automatically generated from version 1.0.2 through the toolbox.v2.0.1 A forgotten foreign key pair have been added.v.2.0.2 The views have been consolidated as tables which allows a considerable improvement in access time.v.2.0.3 The previous version does not include the database. In this version the database has been uploaded.v.2.1.0 Depth images have been updated to 16-bit. Additionally, both the RGB images and the depth images are oriented in the original camera format, i.e. landscape.

The dataset is an annotated point cloud in ASPRS LAS v1.2 format, which is annotated with different classification numbers representing six different road markings, including lane markings (1), pedestrian crosswalk and text (2), bike (3), left arrow (4), right arrow (5), straight arrow (6), and others (0). The point cloud dataset was obtained using Oregon Department of Transportation current mobile lidar system (Leica Pegasus:Two). The data were georeferenced in the supporting software for the Leica Pegasus:Two by Oregon DOT. The authors processed the data to extract the road markings using the road marking extraction tool (Rome2) developed in this Pactrans research.

Spatial Mapping Software Market Outlook

According to our latest research, the global spatial mapping software market size reached USD 6.2 billion in 2024, reflecting the sector’s robust expansion across industries. The market is expected to grow at a CAGR of 14.1% from 2025 to 2033, reaching an estimated USD 19.3 billion by 2033. The primary growth factor propelling this market is the increasing adoption of spatial data analytics and geospatial intelligence across urban planning, environmental monitoring, and asset management sectors, as organizations strive for enhanced decision-making and operational efficiency.

One of the most significant growth drivers for the spatial mapping software market is the rapid urbanization witnessed globally. Governments and private entities are investing heavily in smart city initiatives, which require advanced mapping tools for infrastructure planning, traffic management, and resource allocation. The integration of spatial mapping software with IoT devices and sensors is enabling real-time data collection and visualization, thus streamlining urban planning processes. Moreover, the growing need for sustainable development and efficient land use is pushing city planners to leverage spatial mapping solutions for accurate geospatial analysis, zoning, and resource optimization. This trend is expected to continue, with urban centers increasingly relying on spatial intelligence to tackle challenges related to population growth, environmental sustainability, and public safety.

Technological advancements in artificial intelligence, machine learning, and cloud computing are further accelerating the growth of the spatial mapping software market. Modern mapping platforms now offer sophisticated features such as 3D visualization, predictive analytics, and automated data processing, which significantly enhance the value proposition for end-users. These innovations are not only improving the accuracy and usability of spatial data but are also making it accessible to non-technical users through intuitive interfaces and seamless integrations with enterprise resource planning (ERP) and geographic information system (GIS) platforms. Additionally, the proliferation of mobile devices and high-speed internet connectivity has made spatial mapping tools more versatile, enabling field workers and remote teams to access, update, and share geospatial information in real time.

Another critical factor contributing to the market’s expansion is the rising importance of spatial mapping software in disaster management and environmental monitoring. Governments, NGOs, and emergency response teams are increasingly utilizing these platforms to assess risks, plan evacuations, and coordinate relief efforts in the wake of natural disasters such as floods, earthquakes, and wildfires. Spatial mapping software enables the integration of diverse datasets, including satellite imagery, sensor data, and historical records, to create comprehensive risk maps and predictive models. This capability is invaluable for proactive disaster preparedness and rapid response, helping to minimize loss of life and property. Similarly, environmental agencies are leveraging these tools to monitor deforestation, track wildlife movements, and manage natural resources, further boosting market demand.

From a regional perspective, North America currently leads the spatial mapping software market, driven by substantial investments in smart infrastructure, advanced technological adoption, and a mature ecosystem of geospatial solution providers. Europe follows closely, with strong government support for digital transformation in urban planning and environmental sustainability. The Asia Pacific region is emerging as a high-growth market, fueled by rapid urbanization, infrastructure development, and increasing adoption of smart city solutions in countries like China, India, and Japan. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth, supported by government initiatives for modernization and improved disaster management capabilities. These regional dynamics are shaping the competitive landscape and driving innovation in the global spatial mapping software market.

Component Analysis

The spatial mapping software market is segmented by component into software and services. The software segment dominates the market, accounting for the largest share due to the widespread adoption of propriet

The dataset contains lidar, imu and wheel odometry measurements collected using an all-electric 4 wheel robotic vehicle (Gator) in a forest environment at the Queensland Centre for Advanced Technologies (QCAT - CSIRO) in Brisbane, Australia. The dataset also contains a heightmap image constructed from aerial lidar data of the same forest.
This dataset allows users to run the Forest Localisation software and evaluate the results of the presented localisation method. Lineage: The ground view data was collected utilising an all-electric 4 wheel robotic vehicle equipped with a Velodyne VLP-16 laser mounted on a servo-motor, with a 45 degree inclination, spinning around the vertical axis at 0.5Hz. In addition to the lidar scans, imu and wheel odometry measurements were also recorded. The above canopy map (heightmap) was constructed from aerial lidar data captured using a drone also equipped with a spinning mobile lidar sensor.

Change-To-Liabilities Time Series for Shanghai Huace Navigation Technology Ltd. Shanghai Huace Navigation Technology Ltd. engages in the research and development, manufacturing, and integration high-precision satellite navigation and positioning technologies in China and internationally. The company offers global navigation satellite system (GNSS) smart antennas and antennas, controllers and tablets, surveying and mapping software, GNSS sensors, total stations, and data links; handheld laser scanners, airborne LiDAR and mobile mapping systems, and UAV platforms and cameras; USV platforms and hydrographic sensors; SAR systems; and GNSS corrections for use in survey and engineering, 3D mobile mapping, marine surveying, monitoring and infrastructure, and positioning services. It also provides machine control systems for excavators, graders, and dozers; GNSS+INS and IMU sensors; and auto steering, manual guidance, land leveling, and GNSS systems. The company serves the geospatial, machine control, navigation, and agriculture industries. It also engages in property management, investing, and research and development activities. Shanghai Huace Navigation Technology Ltd. was founded in 2003 and is headquartered in Shanghai, China.

Ebitda Time Series for Shanghai Huace Navigation Technology Ltd. Shanghai Huace Navigation Technology Ltd. engages in the research and development, manufacturing, and integration high-precision satellite navigation and positioning technologies in China and internationally. The company offers global navigation satellite system (GNSS) smart antennas and antennas, controllers and tablets, surveying and mapping software, GNSS sensors, total stations, and data links; handheld laser scanners, airborne LiDAR and mobile mapping systems, and UAV platforms and cameras; USV platforms and hydrographic sensors; SAR systems; and GNSS corrections for use in survey and engineering, 3D mobile mapping, marine surveying, monitoring and infrastructure, and positioning services. It also provides machine control systems for excavators, graders, and dozers; GNSS+INS and IMU sensors; and auto steering, manual guidance, land leveling, and GNSS systems. The company serves the geospatial, machine control, navigation, and agriculture industries. It also engages in property management, investing, and research and development activities. Shanghai Huace Navigation Technology Ltd. was founded in 2003 and is headquartered in Shanghai, China.

This digital elevation model (DEM) is a part of a series of DEMs produced for the National Oceanic and Atmospheric Administration Coastal Services Center's Sea Level Rise and Coastal Flooding Impacts Viewer (www.csc.noaa.gov/slr/viewer). This metadata record describes the DEM for Mobile County in Alabama and Escambia, Santa Rosa, and Okaloosa (southern coastal portion only) Counties in Florida. The DEM includes the best available lidar data known to exist at the time of DEM creation for the coastal areas of Mobile County in Alabama and Escambia, Santa Rosa, and Okaloosa (portion) counties in Florida, that met project specification.This DEM is derived from the USGS National Elevation Dataset (NED), US Army Corps of Engineers (USACE) LiDAR data, as well as LiDAR collected for the Northwest Florida Water Management District (NWFWMD) and the Florida Department of Emergency Management (FDEM). NED and USACE data were used only in Mobile County, AL. NWFWMD or FDEM data were used in all other areas. Hydrographic breaklines used in the creation of the DEM were obtained from FDEM and Southwest Florida Water Management District (SWFWMD). This DEM is hydro flattened such that water elevations are less than or equal to 0 meters.This DEM is referenced vertically to the North American Vertical Datum of 1988 (NAVD88) with vertical units of meters and horizontally to the North American Datum of 1983 (NAD83). The resolution of the DEM is approximately 5 meters. This DEM does not include licensed data (Baldwin County, Alabama) that is unavailable for distribution to the general public. As such, the extent of this DEM is different than that of the DEM used by the NOAA Coastal Services Center in creating the inundation data seen in the Sea Level Rise and Coastal Impacts Viewer (www.csc.noaa.gov/slr/viewer).The NOAA Coastal Services Center has developed high-resolution digital elevation models (DEMs) for use in the Center's Sea Level Rise And Coastal Flooding Impacts internet mapping application. These DEMs serve as source datasets used to derive data to visualize the impacts of inundation resulting from sea level rise along the coastal United States and its territories.The dataset is provided "as is," without warranty to its performance, merchantable state, or fitness for any particular purpose. The entire risk associated with the results and performance of this dataset is assumed by the user. This dataset should be used strictly as a planning reference and not for navigation, permitting, or other legal purposes.

Net-Income-From-Continuing-Operations Time Series for Shanghai Huace Navigation Technology Ltd. Shanghai Huace Navigation Technology Ltd. engages in the research and development, manufacturing, and integration high-precision satellite navigation and positioning technologies in China and internationally. The company offers global navigation satellite system (GNSS) smart antennas and antennas, controllers and tablets, surveying and mapping software, GNSS sensors, total stations, and data links; handheld laser scanners, airborne LiDAR and mobile mapping systems, and UAV platforms and cameras; USV platforms and hydrographic sensors; SAR systems; and GNSS corrections for use in survey and engineering, 3D mobile mapping, marine surveying, monitoring and infrastructure, and positioning services. It also provides machine control systems for excavators, graders, and dozers; GNSS+INS and IMU sensors; and auto steering, manual guidance, land leveling, and GNSS systems. The company serves the geospatial, machine control, navigation, and agriculture industries. It also engages in property management, investing, and research and development activities. Shanghai Huace Navigation Technology Ltd. was founded in 2003 and is headquartered in Shanghai, China.

IntroductionThis data repository includes the cleaned dataset for the Pharos application 2023-2024 data collection period (May 2023-March 2024). This dataset includes the full recurring network measurement (RNM), landmark (LM) datasets, as well as the county geographies used for the study catchment area. Also included in this dataset are the necessary software files to clean and visualize the collected data replicating the methods used in our published analysis.Setup and Execution Instructions for ReproductionPrerequisitesPython 3.9.16 (likely compatible, but untested with >3.7)pip (Python package installer)Files Included_main.py - Main execution script_clean_df.py - Data cleaning module_make_viz.py - Visualization module_clean_lms.csv - Landmark measurement data_clean_rnms.csv - Recurring network measurement data_Counties_WesternKenya.json - Geographic boundaries for Western Kenya counties_requirements.txt - Python package dependenciesInstallation and Execution1a. Create a virtual environment (using a virtual environment is recommended, but not required)python3 -m venv venv1b. Activate the virtual environment (using a virtual environment is recommended, but not required)source venv/bin/activate2. Install required packagespip install -r requirements.txt3. Run the analysispython _main.py4. Deactivate virtual environment when done (if used)deactivate

The French National Mapping Agency (Institut National de l'Information Géographique et Forestière - IGN) is responsible for producing and maintaining the spatial data sets for all of France. At the same time, they must satisfy the needs of different stakeholders who are responsible for decisions at multiple levels from local to national. IGN produces many different maps including detailed road networks and land cover/land use maps over time. The information contained in these maps is crucial for many of the decisions made about urban planning, resource management and landscape restoration as well as other environmental issues in France. Recently, IGN has started the process of creating a high-resolution land use land cover (LULC) maps, aimed at developing smart and accurate monitoring services of LULC over time. To help update and validate the French LULC database, citizens and interested stakeholders can contribute using the Paysages mobile and web applications. This approach presents an opportunity to evaluate the integration of citizens in the IGN process of updating and validating LULC data.

Dataset 1: Change detection validation 2019

This dataset contains web-based validations of changes detected by time series (2016 – 2019) analysis of Sentinel-2 satellite imagery. Validation was conducted using two high resolution orthophotos from respectively 2016 and 2019 as reference data. Two tools have been used: Paysages web application and LACO-Wiki. Both tools used the same validation design: blind validation and the same options. For each detected change, contributors are asked to validate if there is a change and if it is the case then to choose a LU or LC class from a pre-defined list of classes.

The dataset has the following characteristics:

Time period of the change detection: 2016-2019.

Time period of data collection: February 2019-December 2019

Total number of contributors: 105

Number of validated changes: 1048; each change was validated by between 1 to 6 contributors.

Region of interest: Toulouse and surrounding areas

Associated files: 1- Change validation locations.png, 1-Change validation 2019 – Attributes.csv, 1-Change validation 2019.csv, 1-Change validation 2019.geoJSON

This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the LandSense Citizen Observatory, IGN-France, and GeoVille.

Dataset 2: Land use classification 2019

The aim of this data collection campaign was to improve the LU classification of authoritative LULC data (OCS-GE 2016 ©IGN) for built-up area. Using the Paysages web platform, contributors are asked to choose a land use value among a list of pre-defined values for each location.

The dataset has the following characteristics:

Time period of data collection: August 2019

Types of contributors: Surveyors from the production department of IGN

Total number of contributors: 5

Total number of observations: 2711

Data specifications of the OCS-GE ©IGN

Region of interest: Toulouse and surrounding areas

Associated files: 2- LU classification points.png, 2-LU classification 2019 – Attributes.csv, 2-LU classification 2019.csv, 2-LU classification 2019.geoJSON

This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the LandSense Citizen Observatory, IGN-France and the International Institute for Applied Systems Analysis.

Dataset 3: In-situ validation 2018

The aim of this data collection campaign was to collect in-situ (ground-based) information, using the Paysages mobile application, to update authoritative LULC data. Contributors visit pre-determined locations, take photographs, of the point location and in the four cardinal directions away from the point and answer a few questions with respect with the task. Two tasks were defined:

Classify the point by choosing a LU class between three classes: industrial (US2), commercial (US3) or residential (US5).

Validate changes detected by the LandSense Change Detection Service: for each new detected change, the contributor was requested to validate the change and choose a LU and LC class from a pre-defined list of classes.

The dataset has the following characteristics

Time period of data collection: June 2018 – October 2018

Types of contributors: students from the School of Agricultural and Life Sciences and citizens

Total number of contributors: 26

Total number of observations: 281

Total number of photos: 421

Region of interest: Toulouse and surrounding areas

Associated files: 3- Insitu locations.png, 3- Insitu validation 2018 – Attributes.csv, 3- Insitu validation 2018.csv, 3- Insitu validation 2018.geoJSON

This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the LandSense Citizen Observatory, IGN-France.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 689812.

Mobile Robot Dataset Versioning Market Outlook

According to our latest research, the global mobile robot dataset versioning market size reached USD 412 million in 2024, and is expected to grow at a robust CAGR of 16.2% during the forecast period, reaching approximately USD 1.15 billion by 2033. This growth is primarily driven by the increasing adoption of mobile robots across diverse industries and the critical need for robust dataset management solutions to ensure accurate training, deployment, and continuous improvement of autonomous systems. The proliferation of AI-powered robots and rapid advancements in machine learning algorithms are further fueling the demand for sophisticated dataset versioning platforms, enabling organizations to manage, track, and audit data changes efficiently.

One of the most significant growth factors for the mobile robot dataset versioning market is the exponential increase in the deployment of autonomous robots in industries such as logistics, manufacturing, and healthcare. As these robots become more sophisticated, the datasets required for their training and operation also become larger and more complex. Accurate dataset versioning ensures that every iteration of training and operational data is meticulously tracked, which is essential for regulatory compliance, quality assurance, and continuous performance improvement. Companies are increasingly recognizing the role of dataset versioning in minimizing errors, reducing operational downtime, and accelerating the development lifecycle of autonomous systems. The ability to roll back to previous dataset versions or audit changes has become a vital requirement, especially in safety-critical applications.

Another key driver is the rise of collaborative robotics and multi-robot systems, which generate vast amounts of heterogeneous data from diverse sources such as sensors, cameras, and LIDAR. Managing these datasets in real time, especially when updates and modifications are frequent, necessitates advanced versioning solutions that can handle distributed environments. The growing emphasis on data quality, integrity, and traceability is pushing organizations to invest in specialized software and services that provide granular control over dataset modifications. Furthermore, the integration of cloud-based platforms with dataset versioning capabilities allows for seamless collaboration among geographically dispersed teams, thus enhancing productivity and innovation in robot development and deployment.

The market is also benefiting from increased research activities in academia and industry, focusing on improving the accuracy and efficiency of autonomous navigation, mapping, and object recognition. These research initiatives generate vast volumes of experimental data that must be versioned and managed efficiently to support reproducibility and peer collaboration. The growing adoption of open-source frameworks and standardized dataset management practices is further catalyzing market growth. At the same time, regulatory requirements for data transparency and auditability in sectors like healthcare and defense are compelling organizations to adopt advanced dataset versioning solutions, ensuring that all data used in robot training and operation is properly documented and traceable.

From a regional perspective, North America and Europe currently dominate the mobile robot dataset versioning market, driven by robust investments in robotics research, a strong presence of technology vendors, and early adoption of advanced data management solutions. However, the Asia Pacific region is emerging as the fastest-growing market, propelled by rapid industrialization, increased automation in manufacturing and logistics, and significant government initiatives supporting AI and robotics innovation. The Middle East & Africa and Latin America are also witnessing steady growth, albeit from a smaller base, as organizations in these regions increasingly recognize the benefits of dataset versioning in optimizing robot performance and ensuring data compliance. The global landscape is thus characterized by a dynamic interplay of technological advancement, regulatory evolution, and industry-specific adoption patterns.

Component Analysis

The component segment of the mobile robot dataset versioning market is divided into software, hardware, and services, each playing a distinct role in the ecosystem. Software solutions form the backb

This forest health dataset includes both polygon and point data from the current year . Points have a buffered area based on tree number. Surveyors from the Alaska Division of Forestry & Fire Protection and USDA Forest Service - Forest Health Protection document insect, disease, and abiotic damage in the forest from about 1000 feet altitude using a digital mobile sketch-mapping tablet and software. The aerial survey covers about 15% of the forests statewide each year. Note that much of the forest damage documented during these surveys does not typically result in tree or shrub mortality. Aerial survey data disclaimer: USDA Forest Service - Forest Health Protection and the Alaska Division of Forestry make every attempt to accurately identify and locate foreset damage. The data is offered 'as is'.

Code and data for replicating the application study. See S1 Appendix for further details. (ZIP)

Interest-Expense Time Series for Hexagon AB (publ). Hexagon AB (publ) provides geospatial and industrial enterprise solutions worldwide. It operates in Manufacturing Intelligence, Asset Lifecycle Intelligence, Geosystems, Autonomous Solutions, and Safety, structure & Geospatial segments. The company offers analysis and management, machine control, embedded electronics, monitoring, and planning and optimization solutions to agriculture division; design and visualization, asset lifecycle information and outage management, engineering and schematics, enterprise project performance, smart digital, utility GIS, OT/ICS cyber security, operation and maintenance, procurement, fabrication, and construction services for asset lifecycle intelligence division; anti-jam systems, correction services, GNSS and SMART antennas, GNSS/INS receivers and post processing, offroad anatomy, resilience and integrity technology, and visualization software for autonomy and positioning division; and AEC and survey software, airborne, digital realities platform, documentation and verification, geospatial content, machine control, laser scanning and measurement tools, levels, total stations, monitoring, document and verification solutions, detection, GNSS, and mobile mapping system to geosystem division. It also provides CAD CAM and CAE software, CNC simulation and computed tomography software, measurement and inspection hardware and software, manufacturing project management, digital transformation for manufacturing, environmental health and safety, and quality management systems to manufacturing intelligence division; and evaluation, planning and design, drill and blast, load and haul, survey and monitoring, processing, reclamation, safety, autonomous operations, and insights services to mining division. In addition, the company offers GIS, imagery analysis and data management, collaboration, government, transportation, and geospatial and public safety platform solutions. The company was founded in 1975 and is based in Stockholm, Sweden.

An aerial infrared imaging flight was performed at the Cary Institute of Ecological Studies property by Davis Aviation to estimate the minimum number of deer present on the property. This data is useful for management of the Cary deer herd. Flights were conducted on the nights of 30 and 31 January 2020. Imaging was accomplished with a single-engine Cessna 182 airplane and using a high-resolution Mitsubishi M-600 thermal imager oriented ‘looking’ straight down through a camera hole in the belly of the airplane. The thermal imager NTSC video output was routed through a video encoder-decoder (VED) that labeled the imagery with a continuous stream of GPS-derived position, time, date, speed and altitude information. After the flight, imagery was analyzed using a TV monitor and a computer monitor. As the imagery played, the VED decoded the bar-coded GPS signal that was received from the GPS during the flight. The VED recreated the original GPS signal and sent it to the computer so the mobile mapping software ‘thinks’ it is receiving a live signal. The mapping software shows the moving position of the airplane superimposed over aerial imagery of the count area on the computer screen while the recorded infrared imagery of the area below the airplane is visible on the TV monitor. The GPS updates the airplane position once per second throughout the flight and at the same rate during the post-flight analysis. The entire recorded imagery has been reviewed and individual deer identified and marked on the map. Contact the author for data files. Fargionem@caryinstitute.org Description of files:Cary deer count report 30-31 Jan 2020.pdfReport describing methods and results of January 30,31 2020 aerial infrared flight of Cary propertyPDFCary_IR_Deer_1-31-2020_Map.pdfPDF of ArcGIS map of Cary property features overlaid with deer and turkey locations PDFCary_IR_Deer_1-31-2020_Airphoto_Map.pdfPDF of ArcGIS map of Cary property features including airphotos overlaid with deer and turkey locations PDFCary deer points 30_31 Jan20.kmlFile containing deer and turkey locations from aerial infrared flight in Google Earth-readable format that was used to create ArcGIS shapefilesKeyhole Marking Language (KML)

For optimizing production yield while limiting negative environmental impact, sustainable agriculture benefits greatly from real-time, on-the-spot analysis of soil at low cost. Colorimetric paper sensors are ideal candidates for cheap and rapid chemical spot testing. However, their field application requires previously unattained paper sensor reliability and automated readout and analysis by means of integrated mobile communication, artificial intelligence, and cloud computing technologies. Here, we report such a mobile chemical analysis system based on colorimetric paper sensors that operates under tropical field conditions. By mapping topsoil pH in a field with an area of 9 hectares, we have benchmarked the mobile system against precision agriculture standards following a protocol with reference analysis of compound soil samples. As compared with routine lab analysis, our mobile soil analysis system has correctly classified soil pH in 97.8% of cases (132 out of 135 tests) while reducing the analysis turnaround time from days to minutes. Moreover, by performing on-the-spot analyses of individual compound sub-samples in the field, we have achieved a 9-fold increase of spatial resolution that reveals pH-variations not detectable in compound mapping mode. Our mobile system can be extended to perform multi-parameter chemical tests of soil nutrients for applications in environmental monitoring at marginal manufacturing cost. This record comprises a data set of approximately 800 images of a colorimetric paper-based chemical analysis device captured with a custom mobile application at outdoor conditions. The data set also contains three csv files collecting the color information (RGB) values and analysis results (pH values) as determined by the mobile application models from those paper-based device images. These csv files correspond to the RGB data and associated pH values as captured on the field (pre-processed), after processing post field test and a set corresponding to the measurements on a compound sample combining the 9 soil samples collected per hectare. An additional data set is included corresponding to the images and RGB data used for the calibration of the logistic regression models used by the mobile application to predict pH values from the colorimetric information.
Python code is made available for the analysis of the colorimetric chemical reaction on paper of chemical reagents to soil samples across a range of acidity values, including the application of an illumination compensation method, and for the training of predictive machine learning models.

For optimizing production yield while limiting negative environmental impact, sustainable agriculture benefits greatly from real-time, on-the-spot analysis of soil at low cost. Colorimetric paper sensors are ideal candidates for cheap and rapid chemical spot testing. However, their field application requires previously unattained paper sensor reliability and automated readout and analysis by means of integrated mobile communication, artificial intelligence, and cloud computing technologies. Here, we report such a mobile chemical analysis system based on colorimetric paper sensors that operates under tropical field conditions. By mapping topsoil pH in a field with an area of 9 hectares, we have benchmarked the mobile system against precision agriculture standards following a protocol with reference analysis of compound soil samples. As compared with routine lab analysis, our mobile soil analysis system has correctly classified soil pH in 97% of cases while reducing the analysis turnaround time from days to minutes. Moreover, by performing on-the-spot analyses of individual compound sub-samples in the field, we have achieved a 9-fold increase of spatial resolution that reveals pH-variations not detectable in compound mapping mode. Our mobile system can be extended to perform multi-parameter chemical tests of soil nutrients for applications in environmental monitoring at marginal manufacturing cost. This record comprises a data set of approximately 800 images of a colorimetric paper-based chemical analysis device captured with a custom mobile application at outdoor conditions. The data set also contains three csv files collecting the color information (RGB) values and analysis results (pH values) as determined by the mobile application models from those paper-based device images. These csv files correspond to the RGB data and associated pH values as captured on the field (pre-processed), after processing post field test and a set corresponding to the measurements on a compound sample combining the 9 soil samples collected per hectare. An additional data set is included corresponding to the images and RGB data used for the calibration of the logistic regression models used by the mobile application to predict pH values from the colorimetric information.
Python code is made available for the analysis of the colorimetric chemical reaction on paper of chemical reagents to soil samples across a range of acidity values, including the application of an illumination compensation method, and for the training of predictive machine learning models.

Metadata information

Full Title

Data Fusion from Airborne Hyperspectral Data, Airborne LiDAR Data and Aerial photographs at Aramo, Spain

Fusion of different airborne remote sensed and already processed data gathered from color aerial photography, LiDAR and hyperspectral data acquisition over the Aramo site in Spain.

Abstract

This dataset comprises results from the S34I Project, derived from processing of airborne hyperspectral data, airborne LiDAR data and color aerial imagery acquired at the Aramo pilot site in Spain. This document describes processing of color imagery, production of color orthophoto, processing of LiDAR data, and fusion of these data with processed and classified thematic hyperspectral data.

Eurosense conducted complex airborne data acquisition in two consecutive days 30.09.2023 and 01.10.2023 using Riegl LM7800-9184 LiDAR sensor and IGI Digicam H4D-50 medium format RGB camera. 1,645 high resolution RGB images were collected over 24 flight lines. Eurosense produced LiDAR point cloud and color orthophoto mosaic.LiDAR data processing:

Description of the software’s used

AeroOffice and GrafNav – software used for direct georeferencing of mobile and aerial mapping sensors using GNSS and inertial technology.

SDCimport applies the so-called ONLINE Full Waveform Analysis to the digitized echo signals provided by the laser scanner and additionally transforms the geometry data (i.e., range and scan angle) into Cartesian coordinates. The output is a point cloud in the well-defined Scanner's Own Coordinate System (SOCS) with additional descriptors for every point, e.g., a precise time stamp, the echo signal intensity, the echo pulse width, a classification according to first, second, up to last target.

RiWorld transforms the scan data into the coordinate system of the position and orientation data set, usually ETRS89 of WGS84 geocentric. It thus provides the acquired laser data of the object's surfaces within a geocentric coordinate system for further processing. In that case the final coordinate system was WGS84 UTM30N – GRS80.

TerraMatch fixes systematic orientation errors in airborne laser data. It measures the differences between laser surfaces from overlapping flight lines or differences between laser surfaces and known points. These observed differences are translated into correction values for the system orientation - easting, northing, elevation, heading, roll and/or pitch.

TerraScan is the main application in the Terrasolid Software family for managing and processing all types of point clouds. It offers import and project structuring tools for handling the massive number of points of a laser scanning campaign as well as the corresponding trajectory information. Various classification routines enable the automatic filtering of the point cloud.

Geometric corrections

Its content mainly concerns the geometry of the point cloud and quality control.

Initial setting

At the start of treatment, data was calculated by applying the sensor alignment settings corresponding to the last scanner calibration (boresight angles).

Roll: -0.22300

Pitch: -0.04320

Yaw: 0.00170

Determination of connecting lines

The first operation is the extraction of the tie lines used for the adjustment. They are determined by automatic analysis of the data of the different bands, classified as ground (2) and building (6).

They are extracted after the expedited automatic classification described in the previous paragraph.

Absolute control of altimetry

Absolute control of the altimetry is carried out using field measurements of the reference and control fields.

Elevation reference fields

A set of 6 altimetric reference fields were measured in the field by a surveyor.

Result of the absolute adjustment.

Average dz -0.001

Minimum dz: -0.091

Maximum dz: 0.089

Average magnitude: 0.026

Root mean square: 0.034

Std deviation: 0.034

Classification

The delivered classification contains class “Ground” (2), “Vegetation” (4), “Building” (6), “Water” (9) and class 1 “Unclassified”, based on the ASPRS standard.

Evaluation of LiDAR processing results

Absolute height

Both the connection fields and the independent control fields fit within the height tolerances. Global average difference on control fields it is less than -0.001 cm.

Point density and data coverage.

The covered area meets the point density requirement of 10 pts/sqrm.

All checks show that the data meets the accuracy specifications of an accurate LiDAR project.

Orthoprocessing:Triangulation is needed for precise positioning of aerial photographs. The full camera calibration performed because the practice shows that it is necessary for medium format cameras. The control points were collected from point cloud on such objects which were well recognizable in point cloud and also on aerial photographs. For the full area 43 control points are defined and measured in both datasets. The control points coordinate mean residuals are the following in the result of aerial triangulation adjustment: rmsx =0.18 m; rmsy =0.17 m; rmsz =0.26 m.Because of double flights (opposite directions on same flight lines) gave the possibility to produce dsm based ortho-mosaic in 25cm ground resolution.

Data fusion of different sensors data (Postprocessing)The generated raster data are delivered as georeferenced TIFF files. These raster data are covering 116 km² from LiDAR data and 114.6 km² from aerial photographs with a spatial resolution of 1.2 m per pixel. The no-data value is set to -9999, representing areas which are outside of photo and LiDAR coverage. The projected coordinate system is UTM Zone 30 Northern Hemisphere WGS 1984, EPSG 4326.

Generated LiDAR raster data and aerial ortho-mosaic image down-sampled to hyperspectral band ratio mosaics resolution (which has the following pixel size x: ~1.2m y: ~1.09m).Generated raster from point cloud are the following: Intensity, Digital Terrain Model, Digital Surface Model.Intensity band had been interpolated with average method while DTM (from class 2) and DSM (from class 2,4,6,9) with IDW methods. RGB true color composite ortho-mosaic resampled to 1.2m. The ortho-mosaic R, G, B bands are separated to 3 single bands and reformatted to float pixel type and no-data value set to -9999

All bands of three sensors, merged into one composite image with following bands and with the following short names:BRn Band1 – 9 Band ratio of hyperspectral data according to former document (https://zenodo.org/uploads/14193286) BR1 - BR9

LDint Band10 LiDAR intensity raster

LDdtm Band11 DTM layer generated from LiDAR data class 2

LDdsm Band12 DSM layer generated from LiDAR data class 2,4,6,9

OmosR, OmosG, OmosB Band13,14,15 are R G B channels of true color ortho-mosaic of aerial images

Keywords

Earth Observation, Remote Sensing, Hyperspectral Imaging, Automated Processing, Hyperspectral Data Processing, Mineral Exploration, Critical Raw Materials

Pilot area

Aramo

Language

English

URL Zenodo

https://zenodo.org/uploads/xxxxxxxxx

Temporal reference

Acquisition date (dd.mm.yyyy)

30.09.2023; 01.10.2023

Upload date (dd.mm.yyyy)

04.02.2025

Quality and validity

Format

GeoTiff

Spatial resolution

1.2m

Positional accuracy

0.5m

Coordinate system

EPGS 4326

Access and use constrains

Use limitation

None

Access constraint

None

Public/Private

Public

Responsible organisation

Responsible Party

EUROSENSE - Esri Belux

Responsible Contact

Victoria Jadot

Metadata on metadata

Contact

victoria.jadot@eurosense.com

Metadata language

English

Inventory-Turnover Time Series for Hexagon AB (publ). Hexagon AB (publ) provides geospatial and industrial enterprise solutions worldwide. It operates in Manufacturing Intelligence, Asset Lifecycle Intelligence, Geosystems, Autonomous Solutions, and Safety, structure & Geospatial segments. The company offers analysis and management, machine control, embedded electronics, monitoring, and planning and optimization solutions to agriculture division; design and visualization, asset lifecycle information and outage management, engineering and schematics, enterprise project performance, smart digital, utility GIS, OT/ICS cyber security, operation and maintenance, procurement, fabrication, and construction services for asset lifecycle intelligence division; anti-jam systems, correction services, GNSS and SMART antennas, GNSS/INS receivers and post processing, offroad anatomy, resilience and integrity technology, and visualization software for autonomy and positioning division; and AEC and survey software, airborne, digital realities platform, documentation and verification, geospatial content, machine control, laser scanning and measurement tools, levels, total stations, monitoring, document and verification solutions, detection, GNSS, and mobile mapping system to geosystem division. It also provides CAD CAM and CAE software, CNC simulation and computed tomography software, measurement and inspection hardware and software, manufacturing project management, digital transformation for manufacturing, environmental health and safety, and quality management systems to manufacturing intelligence division; and evaluation, planning and design, drill and blast, load and haul, survey and monitoring, processing, reclamation, safety, autonomous operations, and insights services to mining division. In addition, the company offers GIS, imagery analysis and data management, collaboration, government, transportation, and geospatial and public safety platform solutions. The company was founded in 1975 and is based in Stockholm, Sweden.