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I am currently working on a project with my university and ran into trouble. I am building an application for indoor localization and am currently trying to benchmark our implementation. To do this I use some data sets with reference values which contain the latitude and longitude of a scanned point, but I can't figure out, how to interpret these kind of coordinates:

Latitude: 4864889.6629166845
Longitude: -7515.916799399859

Here is the link to the data sheet.

Maybe one of you guys has some experience with this and can help me out, thanks for your help!


Per request in comments, here's some of the data and explanation in case when the link rots (they always do!):

Attribute Information:

Attribute 001 (WAP001): Intensity value for WAP001. Negative integer values from -104 to 0 and +100. Positive value 100 used if WAP001 was not detected.
....
Attribute 520 (WAP520): Intensity value for WAP520. Negative integer values from -104 to 0 and +100. Positive Vvalue 100 used if WAP520 was not detected.
Attribute 521 (Longitude): Longitude. Negative real values from -7695.9387549299299000 to -7299.786516730871000
Attribute 522 (Latitude): Latitude. Positive real values from 4864745.7450159714 to 4865017.3646842018.
Attribute 523 (Floor): Altitude in floors inside the building. Integer values from 0 to 4.
Attribute 524 (BuildingID): ID to identify the building. Measures were taken in three different buildings. Categorical integer values from 0 to 2.
Attribute 525 (SpaceID): Internal ID number to identify the Space (office, corridor, classroom) where the capture was taken. Categorical integer values.
Attribute 526 (RelativePosition): Relative position with respect to the Space (1 - Inside, 2 - Outside in Front of the door). Categorical integer values.
Attribute 527 (UserID): User identifier (see below). Categorical integer values.
Attribute 528 (PhoneID): Android device identifier (see below). Categorical integer values.
Attribute 529 (Timestamp): UNIX Time when the capture was taken. Integer value.

Data Set Information:

Many real world applications need to know the localization of a user in the world to provide their services. Therefore, automatic user localization has been a hot research topic in the last years. Automatic user localization consists of estimating the position of the user (latitude, longitude and altitude) by using an electronic device, usually a mobile phone. Outdoor localization problem can be solved very accurately thanks to the inclusion of GPS sensors into the mobile devices. However, indoor localization is still an open problem mainly due to the loss of GPS signal in indoor environments. Although, there are some indoor positioning technologies and methodologies, this database is focused on WLAN fingerprint-based ones (also know as WiFi Fingerprinting).

Although there are many papers in the literature trying to solve the indoor localization problem using a WLAN fingerprint-based method, there still exists one important drawback in this field which is the lack of a common database for comparison purposes. So, UJIIndoorLoc database is presented to overcome this gap. We expect that the proposed database will become the reference database to compare different indoor localization methodologies based on WiFi fingerprinting.

The UJIIndoorLoc database covers three buildings of Universitat Jaume I with 4 or more floors and almost 110.000m2. It can be used for classification, e.g. actual building and floor identification, or regression, e.g. actual longitude and latitude estimation. It was created in 2013 by means of more than 20 different users and 25 Android devices. The database consists of 19937 training/reference records (trainingData.csv file) and 1111 validation/test records (validationData.csv file).

The 529 attributes contain the WiFi fingerprint, the coordinates where it was taken, and other useful information.

Each WiFi fingerprint can be characterized by the detected Wireless Access Points (WAPs) and the corresponding Received Signal Strength Intensity (RSSI). The intensity values are represented as negative integer values ranging -104dBm (extremely poor signal) to 0dbM. The positive value 100 is used to denote when a WAP was not detected. During the database creation, 520 different WAPs were detected. Thus, the WiFi fingerprint is composed by 520 intensity values.

Then the coordinates (latitude, longitude, floor) and Building ID are provided as the attributes to be predicted.

Additional information has been provided.

The particular space (offices, labs, etc.) and the relative position (inside/outside the space) where the capture was taken have been recorded. Outside means that the capture was taken in front of the door of the space.

Information about who (user), how (android device & version) and when (timestamp) WiFi capture was taken is also recorded.

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  • $\begingroup$ I suspect that's supposed to be 48.648866 N, 75.15916799 W. $\endgroup$ – gerrit Feb 25 at 19:59
  • $\begingroup$ I've already tried that, but the data set was created at university of jaume in Spain (39.995015, -0.069154) which should have at least latitude between ~36 and ~42. But thanks for your try! $\endgroup$ – Trup3s Feb 25 at 20:04
  • $\begingroup$ My guess would be UTM in meters, but assuming zone 31 those coordinates would point to latitude 43N, which is some hundred kilometers North from Universitat Jaume I campus. However, assuming those coordinates to be meters the dataset would span just a few hundred meters, which makes sense for the campus of a small university. Then, my guess is updated as Northing and Easting in meters with a still unknown origin. Anyway, I think the best advice would be to ask the authors - and tell us for completion. $\endgroup$ – Pere Feb 25 at 20:44
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    $\begingroup$ Please copy-paste the important part of the remote link, making your question comprehensible after the remote side goes down. $\endgroup$ – peterh - Reinstate Monica Feb 25 at 23:36
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After quite some research it turns out these are EPSG:3857 WGS 84 / Pseudo-Mercator coordinates.
I found this out by visiting this website where you can transform coordinates from different systems.

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  • $\begingroup$ "Pseudo-Mercator coordinates" wow! $\endgroup$ – uhoh Feb 26 at 1:29

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