Digitise Data

[Adimar Nigerson]

Plotdigitizeris an online data extraction tool that allows users to extract data from images in numerical format. In short, it reverse-engineers your visual graphs into numbers. The software comes with plenty of useful and time-saving features.

Upload the graph image to PlotDigitizer, select the graph type, calibrate the axis/axes, and start marking points and data values of the points that are automatically generated. You can also export these data to other formats. For more, read our official documentation.

Online PlotDigitizer's app is a free tool available for online use only. The tool is free and allows users to extract data from various graphs though it comes with limited features. For full access to additional features, like auto-tracing, dataset storage, you have to purchase a pro license

My current work involves extensive digitising of historic geology data - essentially the creation of new polygons and polylines based on overlain raster and vector layers. This is all done manually using QGIS 2.14.3. I was hoping to get some tips from the community to make this process more efficient and to attain a better outcome.

As it stands now, I am simply tracing features and creating polygons and lines by hand. I've found this quite a frustrating process in QGIS. How do I create perfectly snapped adjacent polygons? Is the only way to play with snapping options (tolerances)? I've tried this but it still seems a bit clunky when you have a large density of vertices, and I still seem to miss some, and/or snap to polygons close by that I want to keep separate. In MapInfo, I could roughly digitise the boundary of the neighbouring polygon (making sure it overlaps) and then erase this overlap with a click of the mouse.

Similarly, I cannot find a tool that will allow me to easily edit the shape of a polygon. In MapInfo, I could simply draw another overlapping (overlaying) polygon, and use the erase tool to erase its extent from the underlying polygon. In QGIS, all I've found that comes close is the add ring feature, however it must be totally enclosed in the polygon being edited. As a result, when I want to edit the shape of a polygon, I find myself manually selecting, moving and adding nodes. This takes forever in some instances, and you have to add and move many many nodes in order to get a smooth result. I probably could draw an overlapping polygon in another layer and then use the layer based geoprocessing tools (Difference?) but I'm hoping there is a more simple solution.

Can anyone help me? I've conducted a fairly extensive search and drawn mostly blanks. I found this which looks like a good technique, but I'm unsure how it would work with multiple overlapping enclosed lines (areas).

Oh my god!!!. Those are the kind of problems that we the geologists have. I tried digitize geological maps with OpenJump. It has much more digitizing tools than QGIS but is difficult. I think digitizing polygons that share boundaries is still a debt for FOSS4G.

I am currently in the process of digitizing old mining maps in QGIS, my strategy is to start with a complete map cover polygon and use the cutting/splitting tool to slice of parts, so that each polygon part is only digitised once.

Digitization is the process of converting analog information into a digital format. In this format, information is organized into discrete units of data called bits that can be separately addressed, usually in multiple-bit groups called bytes.

Digitization gained popularity in the late 20th century with the advent of PCs and the internet. These technologies made it possible to convert many different forms of information -- including text, images, audio and video -- into digital formats.

In the present day, the process of digitization is creating greater opportunities for automation and continues to revolutionize communication, commerce and every aspect of modern life. Especially in the last decade, the rise of digitization has been heavily dependent on newer digital technologies, including cloud computing, machine learning, artificial intelligence (AI), internet of things (IoT) and business intelligence.

The key to a successful enterprise digital transformation is a strong understanding of the desired business goals. Once an organization understands these goals, it can select the right digital technologies and partners to help digitize the business.

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Having recently read an article about digital transformation, it has prompted me to write about the advances in technology and how it has helped companies connect their entire workforce together by seamlessly transferring data from one solution to another.

In the past, a number of disparate solutions would need data from each other to properly complete tasks. This presented challenges, especially for teams that worked in siloed environments. For example, at one of my former employers, we called this confusion the Hairball Effect. If we drew all the various connections between all of the databases being used, it was like a spaghetti junction!

In our case, as was the case with many companies, trying to gain access to the data to be fed into the appropriate solution was a frustrating and time-consuming process. It could take weeks to gain and transfer the data we needed to do our jobs. In the meantime, businesses actions would be delayed, often forcing leaders to make uninformed decisions. This was a very risky endeavor, and oftentimes very costly as well.

Software-as-a-service, or SaaS solutions, have helped to address this problem by providing a resolution. They facilitate the much-needed interdepartmental flow of information, automating what was once a manual process. This relieves stakeholders, significantly shortens the processing cycle of data and improves business agility.

For each photo I have DGPS coordinates of the top left and right corner and the depth of the profile. Thus, I can place the photos in three dimensional space. I want to digitize the soil horizons on each of these photos, combine it with horizon-specific data and then be able to extract the data per coordinate and depth.

Is there a FOSS application that can drape images in 3d and be used to digitize information in 3d vector format? I usually use QGIS for digitizing and R for all my data processing, but I am fine with other FOSS solutions on Linux as long as I can get my data into these applications later on.

Here's a potential approach: I haven't tried this so your mileage may vary, but it should be quite doable (but it might require rather heavy-duty scripting). I'm starting to think in a similar direction for some projects I'm working on.

I'd store the data as Geometry objects in a PostGIS database (keyed to a unique ID per image) but other solutions (shapefiles per-image?) could be used as well. (PostGIS operates exceptionally well with QGIS though...) You can then key your DGPS points to the same image as well, and perhaps have an "image" table that contains the height of your measured section.

From this, you can write a script that puts each geometry through an affine transformation, mapping pixel coordinates to locations in 'real' space on a coordinate grid tied to your DGPS locations and depth. Basically, you map each image corner to a (location, depth) pair and fit your data between them. I won't go into the math here, but my answer here should give the general picture.

I'd do this using a Python script (with the sqlalchemy and geoalchemy2 modules), but it should be possible with a PostGIS SQL script (you'd have to have the image pixel dimensions in your database). You can then write this data to another Geometry column (or shapefile) in the coordinate reference system of your choice. This will leave you with (x,y,z) geometries in a GIS-digestible format.

This is a pretty hands-on method, to be sure, but I think it would leave you with a lot of room to customize, and a pipeline through which you can re-transform your data as you digitize more information.

The Commission recommends Member States to accelerate the digitisation of all cultural heritage monuments and sites, objects and artefacts for future generations, to protect and preserve those at risk, and boost their reuse in domains such as education, sustainable tourism and cultural creative sectors.

This recommendation will contribute to the objectives of the Digital Decade by fostering a secure and sustainable digital infrastructure, digital skills and uptake of technologies by businesses, in particular SMEs.

Europeana, the European digital cultural platform, will be at the basis for building the common data space for cultural heritage. It will allow museums, galleries, libraries, archives across Europe to share and reuse the digitised cultural heritage images such as 3D models of historical sites and high quality scans of paintings.

The tragic burning of Notre Dame Cathedral in Paris showed the importance of digitally preserving culture and the lockdowns highlighted the need for virtually accessible cultural heritage. A robust data infrastructure coupled to easy data pooling and sharing are the necessary ingredients of a common European data space for cultural heritage.

We owe the preservation of our European cultural heritage to future generations. This requires building and deploying our own technological capabilities, empowering people and businesses to enjoy and make the most of this heritage. We must take advantage of the opportunities brought by artificial intelligence, data, and extended reality. The European data space for cultural heritage will promote creation and innovation within the cultural heritage sector, and beyond, in education, tourism, and cultural and creative sectors.

Member States should inform the Commission 24 months from the publication of this Recommendation, and every 2 years thereafter, of actions taken in response to the Recommendation. A newly formed Commission Expert Group on the common European Data Space for Cultural Heritage will monitor the progress of the implementation of the recommendation. Its members are appointed representatives from all Member States.

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