[et_pb_section fb_built=”1″ custom_padding_last_edited=”on|desktop” _builder_version=”4.14.7″ _module_preset=”default” background_color=”#FFFFFF” custom_margin=”||||false|false” custom_padding=”2%|20%|2%|20%|false|true” custom_padding_tablet=”4%|0%|4%|0%|true|true” custom_padding_phone=”6%|0%|6%|0%|true|true” border_color_top=”#e1e1e1″ locked=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.14.7″ _module_preset=”7b1bf5ad-cc2a-4448-981c-4963d88bd6e8″ custom_margin=”||||false|false” custom_padding=”0px||0px||false|true” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.9.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.14.7″ _module_preset=”default” text_text_color=”#000000″ text_line_height=”1.8em” global_colors_info=”{}”]<\/p>\n
In the geospatial industry<\/a>, the ability to handle and exchange geospatial data types efficiently is crucial for professionals sector-wide. From cartographers to urban planners, having a solid understanding of different data formats is essential for effective data management and analysis of geospatial data models.<\/strong><\/p>\n At MASV, our fast and secure file transfer platform is format agnostic, meaning we can send any data format, of any size, from anywhere in the world, for faster time to value<\/a> from remote sensing to storage and geospatial analysis.<\/p>\n Geospatial data types can take all kinds of forms, from raster data to vector data to other data types. Here\u2019s our take on the most popular types of geospatial data and their sizes and backgrounds.<\/strong><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.14.7″ _module_preset=”2514b1ee-af07-4bc3-a96b-c9aaa32f4a18″ text_text_color=”#000000″ text_font_size=”26px” width=”100%” width_tablet=”100%” width_phone=”100%” width_last_edited=”on|tablet” max_width=”100%” custom_margin=”|-54px|0px||false|false” custom_padding=”0px|||0px|false|false” global_colors_info=”{}”]<\/p>\n Table of Contents<\/strong><\/p>\n<\/div>\n<\/div>\n [\/et_pb_text][et_pb_text _builder_version=”4.14.7″ _module_preset=”2514b1ee-af07-4bc3-a96b-c9aaa32f4a18″ text_text_color=”#000000″ text_line_height=”1.8em” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_cta title=”Move Geospatial Data From Sensor to Storage” button_url=”https:\/\/app.massive.io\/en\/signup” button_text=”Start for Free” module_class=”starttrial” _builder_version=”4.14.7″ _module_preset=”default” header_level=”h5″ header_font=”||||||||” header_font_size=”26px” header_line_height=”1.3em” body_line_height=”1.8em” background_color=”#202332″ use_background_color_gradient=”on” background_color_gradient_start=”#072231″ background_color_gradient_end=”#031119″ custom_button=”on” button_text_size=”18px” button_text_color=”#FFFFFF” button_bg_color=”#0472ef” button_bg_color_gradient_start=”#0472ef” button_bg_color_gradient_end=”#005dc6″ button_bg_color_gradient_direction=”90deg” button_border_width=”0px” button_font=”Roboto|700|||||||” button_custom_padding=”10px|42px|10px|42px|true|true” custom_margin=”||20px||false|false” link_option_url=”https:\/\/app.massive.io\/en\/signup” border_radii=”on|10px|10px|10px|10px” border_color_top=”#3d72e7″ border_color_left=”#3d72e7″ box_shadow_style=”preset2″ box_shadow_horizontal=”-13px” box_shadow_style_button=”preset1″ locked=”off” global_colors_info=”{}” button_border_width__hover_enabled=”on|hover” button_custom_padding__hover_enabled=”on|hover” button_custom_padding__hover=”|2em|||false|false” button_border_width__hover=”0px” button_bg_color__hover=”#005dc6″ button_bg_color__hover_enabled=”on|desktop”]<\/p>\n Move large geospatial datasets of any type with MASV’s incredibly powerful, browser-based file transfer.<\/p>\n [\/et_pb_cta][et_pb_code _builder_version=”4.14.7″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_code][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ custom_padding_last_edited=”on|desktop” _builder_version=”4.14.7″ _module_preset=”default” background_color=”#FFFFFF” custom_margin=”||||false|false” custom_padding=”2%|20%|2%|20%|false|true” custom_padding_tablet=”4%|0%|4%|0%|true|true” custom_padding_phone=”6%|0%|6%|0%|true|true” border_width_top=”1px” border_color_top=”#e1e1e1″ locked=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.14.7″ _module_preset=”7b1bf5ad-cc2a-4448-981c-4963d88bd6e8″ custom_margin=”||||false|false” custom_padding=”0px||||false|true” locked=”off” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.9.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_text module_id=”section-one” _builder_version=”4.14.7″ _module_preset=”2514b1ee-af07-4bc3-a96b-c9aaa32f4a18″ text_text_color=”#000000″ text_line_height=”1.8em” header_2_text_color=”#000000″ global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.14.7″ _module_preset=”default” text_text_color=”#000000″ text_line_height=”1.8em” header_3_text_color=”#000000″ global_colors_info=”{}”]<\/p>\n Size<\/strong>: Typically large, often ranging from tens to hundreds of megabytes per file because they store point data from LiDAR geospatial technology.<\/p>\n Description<\/strong>:\u00a0LiDAR data is commonly stored in LAS (or its compressed version, LAZ) format. These files contain point cloud data captured by remote sensing, providing detailed 3D information about the Earth’s surface, making them essential for terrain modeling and environmental data analysis. LiDAR data is often stored in raster data format.<\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/massive.io\/wp-content\/uploads\/2025\/04\/industrial-surveyor-on-construction-site-working-2024-09-19-05-59-14-utc.jpg” alt=”geospatial data types” title_text=”Industrial surveyor on construction site, working with thodolite, gps system and level machine” _builder_version=”4.14.7″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.14.7″ _module_preset=”default” text_text_color=”#000000″ text_line_height=”1.8em” header_3_text_color=”#000000″ global_colors_info=”{}”]<\/p>\n Size<\/strong>: Varies but can be quite large, often tens to hundreds of megabytes, due to storing raster data including spatial imagery.<\/p>\n Description<\/strong>:\u00a0GeoTIFF is a raster data format that embeds geographic information, from sources such as satellite images, into the image file. It is commonly used for storing satellite and aerial imagery, making it a preferred choice for data that requires high resolution.<\/p>\n Size<\/strong>: Size can vary depending on the database and geospatial data being exported, but can range from tens of megabytes to several gigabytes.<\/p>\n Description<\/strong>:\u00a0PostGIS is a spatial extension for PostgreSQL databases that enables the storage and manipulation of data. PostGIS allows for advanced spatial queries and geospatial analysis within a relational database environment.<\/p>\n Size<\/strong>: Can also be quite large, commonly tens to hundreds of megabytes, depending on the complexity of the 2D\/3D CAD data.<\/p>\n Description<\/strong>:\u00a0Developed by Autodesk, DWG is a proprietary format used for storing 2D and 3D design data. While primarily associated with CAD software, DWG files can contain geospatial data, making them valuable for architectural and engineering applications. DWG files are a vector data format, but can also include raster data.<\/p>\n Size<\/strong>: More efficient in size and typically smaller than GeoTIFF and DWG, ranging from a few to tens of megabytes.<\/p>\n Description<\/strong>:\u00a0GeoPackage is an open, standards-based data format that allows for the storage of various types of geospatial data within a single SQLite database file. It is designed to provide a more modern and efficient alternative to traditional formats, like shapefiles.<\/p>\n Size<\/strong>: Usually smaller than GeoTIFFs but can still be tens of megabytes, depending on complexity and number of features.<\/p>\n Description<\/strong>:\u00a0Developed by Esri in the early 1990s, the shapefile format remains one of the most widely used types of geospatial data in GIS. It consists of a collection of files that store geographic data and attributes, such as digital elevation models (a form of raster data). Despite its age, shapefiles continue to be a standard for spatial data interchange.<\/p>\n [\/et_pb_text][et_pb_text module_id=”physical” _builder_version=”4.14.7″ _module_preset=”default” text_text_color=”#000000″ text_line_height=”1.8em” header_4_text_color=”#444444″ background_color=”rgba(158,213,247,0.19)” custom_padding=”2%|3%|2%|3%|false|true” border_width_left=”5px” border_color_left=”#3d72e7″ global_colors_info=”{}”]<\/p>\n \ud83d\udca1 Go Deeper<\/strong>: The Benefits, Challenges, and Best Practices of Cloud Archive<\/a><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.14.7″ _module_preset=”default” text_text_color=”#000000″ text_line_height=”1.8em” header_3_text_color=”#000000″ global_colors_info=”{}”]<\/p>\n Size<\/strong>: Newer, compressed formats of shapefiles. That means they’re smaller on average, possibly ranging from a few to tens of megabytes.<\/p>\n Description<\/strong>:\u00a0A compressed version of the traditional shapefile, SHPX files offer a space-efficient way to store spatial data while maintaining compatibility with GIS software that supports shapefiles.<\/p>\n Size<\/strong>: Another newer, compressed format of shapefiles, SHPZ data is also smaller on average.<\/p>\n Description<\/strong>:\u00a0Similar to SHPX files, SHPZ is a compressed version of shapefiles that helps reduce file size for efficient data storage and transfer.<\/p>\n Size<\/strong>: Similar to other vector formats, typically ranging from a few to tens of megabytes.<\/p>\n Description<\/strong>:\u00a0Developed by MapInfo, TAB files are used to store geospatial data and associated attributes. While primarily associated with MapInfo software, TAB files can be imported into other GIS platforms for geospatial analysis and visualization.<\/p>\n Size<\/strong>: Generally smaller, from kilobytes to a few megabytes depending on the amount of feature data.<\/p>\n Description<\/strong>:\u00a0GeoJSON is a lightweight format for encoding data using JavaScript Object Notation (JSON). It is commonly used for web mapping applications due to its simplicity and compatibility with web technologies.<\/p>\n Size<\/strong>: Similar to GeoJSON, KML\/KMZ data files are usually on the smaller side.<\/p>\n Description<\/strong>: Keyhole Markup Language (KML) and its compressed version (KMZ) are formats developed by Google for displaying geographic data in an Earth browser like Google Earth or Google Maps. These formats are widely used for sharing geospatial data in a visually appealing manner. KML files can represent vector data, such as polygon data.<\/p>\n Size<\/strong>: Like GeoJSON and KML\/KMZ, GML data files are also usually relatively small files.<\/p>\n Description<\/strong>:\u00a0Geography Markup Language (GML) is an XML-based format for encoding geographical data. It is used to represent geographic features and their attributes in a standardized way, making it interoperable across different geographic information systems (GIS) platforms.<\/p>\n Size<\/strong>: A variant of GeoJSON optimized for file size, so they’re typically smaller than a standard GeoJSON \u2014 often ranging from kilobytes to a few megabytes.<\/p>\n Description<\/strong>:\u00a0TopoJSON is a data format for encoding geographic data structures more efficiently than GeoJSON. It optimizes file size by eliminating redundancy in shared boundary lines between features, making it ideal for web mapping applications.<\/p>\n Size<\/strong>: Often used for tabular data, size can vary widely based on content. Generally small to medium-sized, from kilobytes to a few megabytes.<\/p>\n Description<\/strong>:\u00a0While not inherently a geospatial data format, Comma-Separated Values (CSV) files are commonly used for storing tabular data that includes location data such as geographic coordinates. CSV files are versatile and can be easily imported into geographic information systems (GIS) software for geospatial data analysis.<\/p>\n Size<\/strong>: Typically small files, ranging from kilobytes to a few megabytes, often used for lightweight semantic data representations.<\/p>\n Description<\/strong>:\u00a0Resource Description Framework (RDF) is a standard for representing and linking data on the web. In the context of geospatial data, RDF can be used to describe spatial relationships and provide a semantic framework for integrating location data and other geographic information from scanned maps, satellite imagery, or other geospatial data sources.<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_3,2_3″ _builder_version=”4.14.7″ _module_preset=”7b1bf5ad-cc2a-4448-981c-4963d88bd6e8″ background_color=”#f5f5f5″ custom_margin=”||||false|false” custom_padding=”3%|3%|3%|3%|true|true” border_radii=”on|8px|8px|8px|8px” locked=”off” global_colors_info=”{}”][et_pb_column type=”1_3″ _builder_version=”4.9.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/massive.io\/wp-content\/uploads\/2025\/02\/geospatial2.jpg” alt=”Learn how to send files securely with this guide” title_text=”Satellite flying over the Earth atmosphere in Space. 3d Rendering” _builder_version=”4.14.7″ _module_preset=”default” border_radii=”on|5px|5px|5px|5px” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”2_3″ _builder_version=”4.9.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.14.7″ _module_preset=”2514b1ee-af07-4bc3-a96b-c9aaa32f4a18″ text_text_color=”#000000″ text_line_height=”1.8em” header_2_text_color=”#000000″ global_colors_info=”{}”]<\/p>\n Getting geospatial data into S3 or other storage platforms is a bottleneck. Here’s how cloud data transfer can help.<\/p>\n Read more ><\/a><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ custom_padding_last_edited=”on|desktop” _builder_version=”4.14.7″ _module_preset=”default” background_color=”#FFFFFF” custom_margin=”||||false|false” custom_padding=”2%|20%|2%|20%|false|true” custom_padding_tablet=”4%|0%|4%|0%|true|true” custom_padding_phone=”6%|0%|6%|0%|true|true” border_width_top=”1px” border_color_top=”#e1e1e1″ locked=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.14.7″ _module_preset=”7b1bf5ad-cc2a-4448-981c-4963d88bd6e8″ custom_margin=”||||false|false” custom_padding=”0px||||false|true” locked=”off” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.9.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/massive.io\/wp-content\/uploads\/2025\/04\/geospatial-data-1-scaled.jpg” alt=”Placeholder image” title_text=”Center of dispatching maintenance. Portrait of cheerful woman and man working via headset microphone while sitting on navigation controller board” _builder_version=”4.14.7″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][et_pb_text module_id=”section-two” _builder_version=”4.14.7″ _module_preset=”2514b1ee-af07-4bc3-a96b-c9aaa32f4a18″ text_text_color=”#000000″ text_line_height=”1.8em” header_2_text_color=”#000000″ global_colors_info=”{}”]<\/p>\n Data transfer and analysis isn’t worth much if your data quality isn’t up to snuff, which is why the Open Geospatial Consortium (OGC) has put together this database<\/a> of internationally recognized specifications around data quality.<\/p>\n You can also check out the list of geospatial data types from the U.S. Library of Congress<\/a>.<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ custom_padding_last_edited=”on|desktop” _builder_version=”4.14.7″ _module_preset=”default” background_color=”#FFFFFF” custom_margin=”||||false|false” custom_padding=”2%|20%|2%|20%|false|true” custom_padding_tablet=”4%|0%|4%|0%|true|true” custom_padding_phone=”6%|0%|6%|0%|true|true” border_width_top=”1px” border_color_top=”#e1e1e1″ locked=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.14.7″ _module_preset=”7b1bf5ad-cc2a-4448-981c-4963d88bd6e8″ custom_margin=”||||false|false” custom_padding=”0px||||false|true” locked=”off” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.9.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_text module_id=”section-three” _builder_version=”4.14.7″ _module_preset=”2514b1ee-af07-4bc3-a96b-c9aaa32f4a18″ text_text_color=”#000000″ text_line_height=”1.8em” header_2_text_color=”#000000″ global_colors_info=”{}”]<\/p>\n OK, OK\u2026 you got us there. When it comes to drone footage, satellite imagery, aerial photographs, digital elevation models, weather data, land cover data, and other types of geospatial data, there are other photo (raster data) and video formats worth mentioning. We\u2019ll talk about those geospatial data types in a future post.<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ custom_padding_last_edited=”on|phone” _builder_version=”4.14.7″ background_color=”#f5f5f5″ custom_margin=”||||false|false” custom_padding=”2%|20%|2%|20%|true|true” custom_padding_tablet=”4%|0%|4%|0%|true|true” custom_padding_phone=”6%|0%|6%|0%|true|true” global_colors_info=”{}”][et_pb_row _builder_version=”4.14.7″ _module_preset=”7b1bf5ad-cc2a-4448-981c-4963d88bd6e8″ custom_margin=”||||false|false” custom_padding=”||||false|true” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.9.3″ _module_preset=”default” global_colors_info=”{}”][et_pb_text module_id=”section-four” _builder_version=”4.14.7″ _module_preset=”2514b1ee-af07-4bc3-a96b-c9aaa32f4a18″ text_text_color=”#000000″ text_line_height=”1.8em” header_2_text_color=”#000000″ hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n MASV<\/a>‘s secure, cloud-based file transfer can move geospatial data types of any size and type, and connects to cloud and connected on-prem storage with no-code integrations.<\/strong> This enables automated data transfers, from data collection from remote sensing, to storage, to geospatial data analytics without manual actions.<\/p>\n MASV comes with enterprise-grade security and compliance features out of the box, and comes with many speed and reliability features\u2014including Multiconnect<\/a> channel bonding, which allows users to bond internet connections for reliable high-speed file transfer in remote locations with limited connectivity.<\/p>\n If you have it, we can transfer it and probably already have\u2014whatever your data volume or geospatial technology. MASV can transfer file packages of any size quickly and reliably, and already works with several geospatial companies who transfer various geospatial data types from the field to centralized storage every day.<\/p>\n Sign up for MASV<\/a> for free to test your geospatial data transfer workflows right now, or email us at team@masv.io<\/a> to learn more.<\/p>\n [\/et_pb_text][et_pb_cta title=”Ingest to Any Cloud or Connected On-Prem Storage” button_url=”https:\/\/app.massive.io\/en\/signup” button_text=”Start for Free” module_class=”starttrial” _builder_version=”4.14.7″ _module_preset=”default” header_level=”h5″ header_font=”||||||||” header_font_size=”26px” header_line_height=”1.3em” body_line_height=”1.8em” background_color=”#202332″ use_background_color_gradient=”on” background_color_gradient_start=”#072231″ background_color_gradient_end=”#031119″ custom_button=”on” button_text_size=”18px” button_text_color=”#FFFFFF” button_bg_color=”#0472ef” button_bg_color_gradient_start=”#0472ef” button_bg_color_gradient_end=”#005dc6″ button_bg_color_gradient_direction=”90deg” button_border_width=”0px” button_font=”Roboto|700|||||||” button_custom_padding=”10px|42px|10px|42px|true|true” custom_margin=”||20px||false|false” link_option_url=”https:\/\/app.massive.io\/en\/signup” border_radii=”on|10px|10px|10px|10px” border_color_top=”#3d72e7″ border_color_left=”#3d72e7″ box_shadow_style=”preset2″ box_shadow_horizontal=”-13px” box_shadow_style_button=”preset1″ locked=”off” global_colors_info=”{}” button_border_width__hover_enabled=”on|hover” button_custom_padding__hover_enabled=”on|hover” button_custom_padding__hover=”|2em|||false|false” button_border_width__hover=”0px” button_bg_color__hover=”#005dc6″ button_bg_color__hover_enabled=”on|desktop”]<\/p>\n Connect MASV to your favorite storage platform in seconds with no-code integrations. [\/et_pb_cta][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" Un aper\u00e7u des types de donn\u00e9es g\u00e9ospatiales les plus populaires, de leur taille et de leur utilisation - des fichiers LAS\/LAZ aux GeoTIFF en passant par les SHPZ\/SHPX. <\/p>","protected":false},"author":7,"featured_media":126906,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[12],"tags":[],"class_list":["post-126900","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-file-transfer"],"acf":[],"_links":{"self":[{"href":"https:\/\/massive.io\/fr\/wp-json\/wp\/v2\/posts\/126900","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/massive.io\/fr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/massive.io\/fr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/massive.io\/fr\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/massive.io\/fr\/wp-json\/wp\/v2\/comments?post=126900"}],"version-history":[{"count":0,"href":"https:\/\/massive.io\/fr\/wp-json\/wp\/v2\/posts\/126900\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/massive.io\/fr\/wp-json\/wp\/v2\/media\/126906"}],"wp:attachment":[{"href":"https:\/\/massive.io\/fr\/wp-json\/wp\/v2\/media?parent=126900"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/massive.io\/fr\/wp-json\/wp\/v2\/categories?post=126900"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/massive.io\/fr\/wp-json\/wp\/v2\/tags?post=126900"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n
Ranking the World’s Most Popular Geospatial Data Types by Size<\/h2>\n
1. LAS\/LAZ<\/h3>\n
2. GeoTIFF (.tif)<\/h3>\n
3. PostGIS (.sql)<\/h3>\n
4. DWG (.dwg)<\/h3>\n
5. GPKG\/Geopackage (.gpkg)<\/h3>\n
6. Shapefile (.shp)<\/h3>\n
7. SHPX (.shpx)<\/h3>\n
8. SHPZ (.shpz)<\/h3>\n
9. TAB (.tab)<\/h3>\n
10. GeoJSON (.json)<\/h3>\n
11. KML\/KMZ<\/h3>\n
12. GML (.gml)<\/h3>\n
13. TopoJSON (.topojson)<\/h3>\n
14. CSV (.csv)<\/h3>\n
15. RDF (.rdf)<\/h3>\n
How to Accelerate and Improve Geospatial Data Transfer<\/h4>\n
If You Really Want to Nerd Out\u2026<\/h2>\n
Hey, Wait a Minute! Where Are All the Photo and Image Geospatial Data Types?<\/h2>\n
Move Big Geospatial Data, of Any Type, With MASV<\/h2>\n
<\/p>\n