description: This tool provides a no-cost downloadable software tool that allows users to interact with professional quality GIS maps. Users access pre-compiled projects through a free software product called ArcReader, and are able to open and explore HUD-specific project data as well as design and print custom maps. No special software/map skills beyond basic computer skills are required, meaning users can quickly get started working with maps of their communities.; abstract: This tool provides a no-cost downloadable software tool that allows users to interact with professional quality GIS maps. Users access pre-compiled projects through a free software product called ArcReader, and are able to open and explore HUD-specific project data as well as design and print custom maps. No special software/map skills beyond basic computer skills are required, meaning users can quickly get started working with maps of their communities.

Dataset

This dataset was created by Srijan Jain

Released under CC0: Public Domain

Contents

The INTERPNT method can be used to produce accurate maps of trees based solely on tree diameter and tree-to-tree distance measurements. For additional details on the technique please see the published paper (Boose, E. R., E. F. Boose and A. L. Lezberg. 1998. A practical method for mapping trees using distance measurements. Ecology 79: 819-827). Additional information is contained in the documentation that accompanies the program. The Abstract from the paper is reproduced below. "Accurate maps of the locations of trees are useful for many ecological studies but are often difficult to obtain with traditional surveying methods because the trees hinder line of sight measurements. An alternative method, inspired by earlier work of F. Rohlf and J. Archie, is presented. This "Interpoint method" is based solely on tree diameter and tree-to-tree distance measurements. A computer performs the necessary triangulation and detects gross errors. The Interpoint method was used to map trees in seven long-term study plots at the Harvard Forest, ranging from 0.25 ha (200 trees) to 0.80 ha (889 trees). The question of accumulation of error was addressed though a computer simulation designed to model field conditions as closely as possible. The simulation showed that the technique is highly accurate and that errors accumulate quite slowly if measurements are made with reasonable care (e.g., average predicted location errors after 1,000 trees and after 10,000 trees were 9 cm and 15 cm, respectively, for measurement errors comparable to field conditions; similar values were obtained in an independent survey of one of the field plots). The technique requires only measuring tapes, a computer, and two or three field personnel. Previous field experience is not required. The Interpoint method is a good choice for mapping trees where a high level of accuracy is desired, especially where expensive surveying equipment and trained personnel are not available."

TDEC is continuously striving to create better business practices through GIS and one way that we have found to provide information and answer some question is utilizing an interactive map. An interactive map is a display of geospatial data that allows you to manipulate and query the contents to get the information needed using a set of provided tools. Interactive maps are created using GIS software, and then distributed to users, usually over a computer network. The TDEC Land and Water interactive map will allow you to do simple tasks such as pan, zoom, measure and find a lat/long, while also giving you the capability of running simple queries to locate land and waters by name, entity, and number. With the ability to turn off and on back ground images such as aerial imagery (both black and white as well as color), we hope that you can find much utility in the tools provided.

As a first step in understanding law enforcement agencies' use and knowledge of crime mapping, the Crime Mapping Research Center (CMRC) of the National Institute of Justice conducted a nationwide survey to determine which agencies were using geographic information systems (GIS), how they were using them, and, among agencies that were not using GIS, the reasons for that choice. Data were gathered using a survey instrument developed by National Institute of Justice staff, reviewed by practitioners and researchers with crime mapping knowledge, and approved by the Office of Management and Budget. The survey was mailed in March 1997 to a sample of law enforcement agencies in the United States. Surveys were accepted until May 1, 1998. Questions asked of all respondents included type of agency, population of community, number of personnel, types of crimes for which the agency kept incident-based records, types of crime analyses conducted, and whether the agency performed computerized crime mapping. Those agencies that reported using computerized crime mapping were asked which staff conducted the mapping, types of training their staff received in mapping, types of software and computers used, whether the agency used a global positioning system, types of data geocoded and mapped, types of spatial analyses performed and how often, use of hot spot analyses, how mapping results were used, how maps were maintained, whether the department kept an archive of geocoded data, what external data sources were used, whether the agency collaborated with other departments, what types of Department of Justice training would benefit the agency, what problems the agency had encountered in implementing mapping, and which external sources had funded crime mapping at the agency. Departments that reported no use of computerized crime mapping were asked why that was the case, whether they used electronic crime data, what types of software they used, and what types of Department of Justice training would benefit their agencies.

Software is intangible, invisible, and at the same time pervasive in everyday devices, activities, and services accompanying our life. Therefore, citizens hardly realize its complexity, power, and impact in many aspects of their daily life. In this study, we report on one experiment that aims at letting citizens make sense of software presence and activity in their everyday lives, through sound: the invisible complexity of the processes involved in the shutdown of a personal computer. We used sonification to map information embedded in software events into the sound domain. The software events involved in a shutdown have names related to the physical world and its actions: write events (information is saved into digital memories), kill events (running processes are terminated), and exit events (running programs are exited). The research study presented in this article has a “double character.” It is an artistic realization that develops specific aesthetic choices, and it has also pedagogical purposes informing the causal listener about the complexity of software behavior. Two different sound design strategies have been applied: one strategy is influenced by the sonic characteristics of the Glitch music scene, which makes deliberate use of glitch-based sound materials, distortions, aliasing, quantization noise, and all the “failures” of digital technologies; and a second strategy based on the sound samples of a subcontrabass Paetzold recorder, an unusual and special acoustic instrument which unique sound has been investigated in the contemporary art music scene. Analysis of quantitative ratings and qualitative comments of 37 participants revealed that the sound design strategies succeeded in communicating the nature of the computer processes. Participants also showed in general an appreciation of the aesthetics of the peculiar sound models used in this study.

This is a vector tile service with labels for the fine scale vegetation and habitat map, to be used in web maps and GIS software packages. Labels appear at scales greater than 1:5,000 and show the full Latin name or vegetation group name. At scales smaller than 1:5,000 the abbreviated vegetation class name is displayed. This service is mean to be used in conjunction with the vector tile services of the veg map polygons (either the solid symbology service or the hollow symbology service). The key to map class abbreviations can be found here. The Sonoma County fine scale vegetation and habitat map is an 82-class vegetation map of Sonoma County with 212,391 polygons. The fine scale vegetation and habitat map represents the state of the landscape in 2013 and adheres to the National Vegetation Classification System (NVC). The map was designed to be used at scales of 1:5,000 and smaller. The full datasheet for this product is available here: https://sonomaopenspace.egnyte.com/dl/qOm3JEb3tD The final report for the fine scale vegetation map, containing methods and an accuracy assessment, is available here: https://sonomaopenspace.egnyte.com/dl/1SWyCSirE9Class definitions, as well as a dichotomous key for the map classes, can be found in the Sonoma Vegetation and Habitat Map Key (https://sonomaopenspace.egnyte.com/dl/xObbaG6lF8) The fine scale vegetation and habitat map was created using semi-automated methods that include field work, computer-based machine learning, and manual aerial photo interpretation. The vegetation and habitat map was developed by first creating a lifeform map, an 18-class map that served as a foundation for the fine-scale map. The lifeform map was created using “expert systems” rulesets in Trimble Ecognition. These rulesets combine automated image segmentation (stand delineation) with object based image classification techniques. In contrast with machine learning approaches, expert systems rulesets are developed heuristically based on the knowledge of experienced image analysts. Key data sets used in the expert systems rulesets for lifeform included: orthophotography (’11 and ’13), the LiDAR derived Canopy Height Model (CHM), and other LiDAR derived landscape metrics. After it was produced using Ecognition, the preliminary lifeform map product was manually edited by photo interpreters. Manual editing corrected errors where the automated methods produced incorrect results. Edits were made to correct two types of errors: 1) unsatisfactory polygon (stand) delineations and 2) incorrect polygon labels. The mapping team used the lifeform map as the foundation for the finer scale and more floristically detailed Fine Scale Vegetation and Habitat map. For example, a single polygon mapped in the lifeform map as forest might be divided into four polygons in the in the fine scale map including redwood forest, Douglas-fir forest, Oregon white oak forest, and bay forest. The fine scale vegetation and habitat map was developed using a semi-automated approach. The approach combines Ecognition segmentation, extensive field data collection, machine learning, manual editing, and expert review. Ecognition segmentation results in a refinement of the lifeform polygons. Field data collection results in a large number of training polygons labeled with their field-validated map class. Machine learning relies on the field collected data as training data and a stack of GIS datasets as predictor variables. The resulting model is used to create automated fine-scale labels countywide. Machine learning algorithms for this project included both Random Forests and Support Vector Machines (SVMs). Machine learning is followed by extensive manual editing, which is used to 1) edit segment (polygon) labels when they are incorrect and 2) edit segment (polygon) shape when necessary. The map classes in the fine scale vegetation and habitat map generally correspond to the alliance level of the National Vegetation Classification, but some map classes - especially riparian vegetation and herbaceous types - correspond to higher levels of the hierarchy (such as group or macrogroup).

This software contains the v1.0.0 release of Nilas: the south ocean mapping platform (https://nilas.org). This mapping tool (beta) has been developed by the Australian Antarctic Division for the Antarctic sea-ice zone to support their research and operational activities. Nilas displays multiple layers of physical and biogeochemical variables. These variables are primarily derived from remotely sensed products and updated as source data become available. The source code is well documented with both readme files and inline comments. This application is written primarily in javascript and was developed using Node.js, vite and a small amount of vue. The Nilas platform was based on the Leaflet open source library. It can be configured to display other Antarctic related geospatial products including raster and vector data.

See the related record, "AAS_4506_NILAS_DATA" for data from this project.

The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

The 3D modeling market for games and animation is experiencing robust growth, driven by the increasing demand for high-quality visuals in video games, films, and other interactive media. The market, estimated at $15 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by several key factors. Advancements in software capabilities, such as improved rendering engines and real-time ray tracing, are enabling the creation of increasingly realistic and immersive experiences. Furthermore, the rising adoption of virtual and augmented reality (VR/AR) technologies is further boosting demand for sophisticated 3D modeling tools. The mobile gaming segment is a significant contributor to market expansion, with developers continuously seeking innovative ways to enhance the visual appeal of mobile games. The increasing accessibility of powerful hardware, including gaming PCs and high-end mobile devices, is also playing a crucial role. However, market growth is not without challenges. High software costs and the steep learning curve associated with many professional 3D modeling packages can act as barriers to entry for smaller studios and independent developers. Competition among established players like Autodesk, Adobe, and Maxon is intense, leading to price wars and pressure on profit margins. The continuous evolution of technology necessitates ongoing investment in research and development to stay ahead of the curve. Despite these restraints, the long-term outlook for the 3D modeling market in games and animation remains positive, with continued innovation and expanding applications pushing the boundaries of visual fidelity and immersion. The diverse segments, including modeling software, UV tools, and applications in both mobile and computer games, present opportunities for specialized companies and broad market reach.

TIGERweb allows the viewing of TIGER spatial data online and for TIGER data to be streamed to your mapping application. TIGERweb consists of a web mapping service and a REST service. Thew web mapping service is an Open Geospatial Consortium (OGC) service that allows users to visualize our TIGER (Topologically Integrated Geographic Encoding and Referencing database) data. This service consists of two applications and eight services. The applications allow users to select features and view their attributes, to search for features by name or geocode, and to identify features by selecting them from a map. The TIGERweb applications are a simple way to view our TIGER data without having to download the data. The web Mapping services provide a simple HTTP interface for requesting geo-registered map images from our geospatial database. It allows users to produce maps containing TIGERweb layers with layers from other servers. TIGERweb consists of the following two applications and eight services: Applications: TIGERweb, TIGERweb Decennial Services: Current, ACS16, ACS15, ACS14, ACS13, Econ12, Census 2010 (for the TIGERweb application), Physical Features (for the TIGERweb application), Census 2010 (for the TIGERweb Decennial application), Census 2000 and Physical Features (for the TIGERweb Decennial application) The REST service is a way for Web clients to communicate with geographic information system (GIS) servers through Representational State Transfer (REST) technology. It allows users to interface with the REST server with structured URLs using a computer language like PYTHON or JAVA. The server responds with map images, text-based geographic information, or other resources that satisfy the request. There are three groups of services: TIGERweb, TIGERweb Generalized and TIGERweb Decennial. TIGERweb consists of boundaries as of January 1, 2016 while TIGERweb Decennial consists of boundaries as they were of January 1, 2010. TIGERweb Generalized is specifically designed for small-scale thematic mapping. The following REST services are offered for both groups: American Indian, Alaska Native, and Native Hawaiian Areas Census Regions and Divisions Census Tracts and Blocks Legislative Areas Metropolitan and Micropolitan Statistical Areas and Related Statistical Areas Places and County Subdivisions PUMAs, UGAs and ZCTAs School Districts States and Counties Urban Areas The following services are only offered in TIGERweb and TIGERweb Decennial: Hydrography Labels Military and Other Special Land Use Areas Transportation (Roads and Railroads) Tribal Census Tracts and Block Groups The following services is only offered in TIGERweb Generalized: Places and County Subdivisions (Economic Places)

The Traffic Resources web map is used to author the Transportation web experience at https://ebrgis.maps.arcgis.com/home/item.html?id=822fdc16e55e4410b5ee200a5a4a184c.Objective:The data displayed in this web map is intended to provide motorists in the Baton Rouge area with near real-time traffic data.Details:The traffic incidents are being handled by the Baton Rouge Police Department and the East Baton Rouge Sheriff's Office. The information is gathered from the 911 Computer Assisted Dispatch System, and it does not include data from Baker Police, Central Police, Zachary Police, Louisiana State Police, LSU, or Southern University. The map will refresh automatically every 60 seconds.Disclaimer:This page contains raw data and unconfirmed information about traffic incidents as they have been reported to the East Baton Rouge Parish 911 computer aided dispatch (CAD) system. The possibility exists that an incident may have been reported or classified incorrectly.The information on this site is intended only as a general guide to possible traffic related situations being investigated by the Baton Rouge Police Department, East Baton Rouge Sheriff's Office, Emergency Medical Services, or Baton Rouge Fire Department. Incidents located in East Baton Rouge Parish that are being investigated by other agencies, including Baker Police, Central Police, Zachary Police, Louisiana State Police, or campus police at LSU and Southern University incidents are not reflected in this map. Because this information is derived from the 911 CAD system, the incident will remain on the page until the responding officer has been taken off the call. This may occur after the actual incident has been cleared from the roadway. Also, traffic incident points should be considered approximate, not exact locations.The City-Parish Department of Transportation and Drainage maintains the local road closure data which is displayed in this web map. More information about local road closures can be found on the EBR Road Closures webpage. This map displays data from the Louisiana Department of Transportation and Development. Included are the traffic camera locations and LA-DOTD road closures. These datasets are provided as a convenience for users. The City-Parish is not responsible for the information provided by LA-DOTD or its affiliates.The live and predictive traffic data comes directly from HERE. HERE collects data per month, and where available, users sensors to augment the collected data. An algorithm compiles the data and computes accurate speeds. The imagery is not live. Imagery is provided as a service from Esri, Inc. and image tiles are not updated each time the map is displayed.This site uses web mapping software from Esri, Inc. which has been purchased by the Baton Rouge City-Parish Government.Users are encouraged to submit their questions and comments related to the EBRGIS Program by sending an email to gis@brla.gov or contacting the Department of Information Services at (225) 389-3070.

The GPS Bike Computers Market Report is Segmented by Type (Mapping and Non-Mapping), Application (Athletics and Sports, Fitness and Commuting, and Recreational/Leisure), and Geography (North America, Europe, Asia-Pacific, and Rest of the World). The Report Offers Market Size and Forecasts in Value (USD) for all the Above Segments.

The datasets are in MID/MIF formats to be processed in QGIS with use of self-written open source software. The datasets are used to model single or multiple socio-economic scenarios of regional spatial development and to build graded suitability maps.

The datasets contain:

• the 10x10 km grid, topographic layers (navigable rivers, railways, paved roads, settlements, and river ports), and semantic description of each unit area of the Novosibirsk region;
• thematic maps (accessibility maps) on navigational rivers, paved roads, railways, river ports, and settlements.

The GEBCO bathymetric grid is accompanied by a Type Identifier (TID) Grid, the function of which is to identify the source data type that the corresponding grid cell in the bathymetric grid is based on. The aim is to allow users to assess the ‘quality’ of the grid in a particular area, i.e. if it is based on multibeam data, singlebeam data or on interpolation, etc.For more GEBCO related layers and maps please visit the GEBCO ArcGIS Online Group.More infoTID codes The table below details the coding of the GEBCO Type Identifier (TID) grid. TID Definition

0 Land

Direct measurements

10 Singlebeam - depth value collected by a single beam echo-sounder

11 Multibeam - depth value collected by a multibeam echo-sounder

12 Seismic - depth value collected by seismic methods

13 Isolated sounding - depth value that is not part of a regular survey or trackline

14 ENC sounding - depth value extracted from an Electronic Navigation Chart (ENC)

15 Lidar - depth derived from a bathymetric lidar sensor

16 Depth measured by optical light sensor

17 Combination of direct measurement methods

Indirect measurements

40 Predicted based on satellite-derived gravity data - depth value is an interpolated value guided by satellite-derived gravity data

41 Interpolated based on a computer algorithm - depth value is an interpolated value based on a computer algorithm (e.g. Generic Mapping Tools)

42 Digital bathymetric contours from charts - depth value taken from a bathymetric contour data set

43 Digital bathymetric contours from ENCs - depth value taken from bathymetric contours from an Electronic Navigation Chart (ENC)

44 Bathymetric sounding - depth value at this location is constrained by bathymetric sounding(s) within a gridded data set where interpolation between sounding points is guided by satellite-derived gravity data

45 Predicted based on helicopter/flight-derived gravity data

46 Depth estimated by calculating the draft of a grounded iceberg using satellite-derived freeboard measurement.

Unknown

70 Pre-generated grid - depth value is taken from a pre-generated grid that is based on mixed source data types, e.g. single beam, multibeam, interpolation etc.

71 Unknown source - depth value from an unknown source

72 Steering points - depth value used to constrain the grid in areas of poor data coverage

Pearson's correlation coefficients of mapping qualities.

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 4th,2023. Listing of computer software for the gene mapping community on the following topics: genetic linkage analysis for human pedigree data, QTL analysis for animal/plant breeding data, genetic marker ordering, genetic association analysis, haplotype construction, pedigree drawing, and population genetics. The inclusion of a program should not be interpreted as an endorsement to that program from us. In the last few years, new technology produces new types of genetic data, and the scope of genetic analyses change dramatically. It is no longer obvious whether a program should be included or excluded from this list. Topics such as next-generation-sequencing (NGS), gene expression, genomics annotation, etc. can all be relevant to a genetic study, yet be specialized topics by themselves. Though programs on variance calling from NSG can be in, those can sequence alignment might be out; programs on eQTL can be in, those on differential expression might be out. This page was created by Dr. Wentian Li, when he was at Columbia University (1995-1996). It was later moved to Rockefeller University (1996-2002), and now takes its new home at North Shore LIJ Research Institute (2002-now). The present copy is maintained by Jurg Ott as a single file. More than 240 programs have been listed by December 2004, more than 350 programs by August 2005, close to 400 programs by December 2006, and close to 480 programs by November 2008, and over 600 programs by October 2012. A version of the searchable database was developed by Zhiliang Hu of Iowa State University, and a recent round of updating was assisted by Wei JIANG of Harbin Medical School. Some earlier software can be downloaded from EBI: ftp://ftp.ebi.ac.uk/pub/software/linkage_and_mapping/ (Linkage and Mapping Software Repository), and http://genamics.com/software/index.htm may contain archived copy of some programs.

Mapping-by-sequencing strategies combine next-generation sequencing (NGS) with classical linkage analysis, allowing rapid identification of the causal mutations of the phenotypes exhibited by mutants isolated in a genetic screen. Computer programs that analyze NGS data obtained from a mapping population of individuals derived from a mutant of interest to identify a causal mutation are available; however, the installation and usage of such programs requires bioinformatic skills, modifying or combining pieces of existing software, or purchasing licenses. To ease this process, we developed Easymap, an open-source program that simplifies the data analysis workflows from raw NGS reads to candidate mutations. Easymap can perform bulked segregant mapping of point mutations induced by ethyl methanesulfonate (EMS) with DNA-seq or RNA-seq datasets, as well as tagged-sequence mapping for large insertions, such as transposons or T-DNAs. The mapping analyses implemented in Easymap have been validated with experimental and simulated datasets from different plant and animal model species. Easymap was designed to be accessible to all users regardless of their bioinformatics skills by implementing a user-friendly graphical interface, a simple universal installation script, and detailed mapping reports, including informative images and complementary data for assessment of the mapping results. Easymap is available at http://genetics.edu.umh.es/resources/easymap; its Quickstart Installation Guide details the recommended procedure for installation.

this dataset was collected by an unmanned aerial vehicle in hermitage, réunion - 2023-12-01.underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. these images can be annotated and shared to train ia models which can in turn predict the objects on the images. we provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.survey information camera: hasselblad l1d-20c number of images: 119 total size : 1.04 gb flight start: 2023:12:01 15:07:31 flight end: 2023:12:01 15:13:31 flight duration: 0h 6min 0sec median height: 79.9 m area covered: 3.93 hageneric folder structureyyyymmdd_countrycode-optionalplace_device_session-number├── dcim : folder to store videos and photos depending on the media collected.├── gps : folder to store any positioning related file. if any kind of correction is possible on files (e.g. post-processed kinematic thanks to rinex data) then the distinction between device data and base data is made. if, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the gps folder.│ ├── base : files coming from rtk station or any static positioning instrument.│ └── device : files coming from the device.├── metadata : folder with general information files about the session.├── processed_data : contain all the folders needed to store the results of the data processing of the current session.│ ├── bathy : output folder for bathymetry raw data extracted from mission logs.│ ├── frames : output folder for georeferenced frames extracted from dcim videos.│ ├── ia : destination folder for image recognition predictions.│ └── photogrammetry : destination folder for reconstructed models in photogrammetry.└── sensors : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.).softwareall the raw data was processed using our worflow.all predictions were generated by our inference pipeline.you can find all the necessary scripts to download this data in this repository.enjoy your data with seatizendoi!

The report covers Law Enforcement Software Companies and it is segmented by Solutions (Records Management Systems, Computer Aided Dispatch Systems, GIS/Mapping, Emergency Response, Jail Management, Evidence Management, Video Analytics), Deployment (Cloud, On-Premise), and Geography.