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Earth and Nature,Geography Classification

数据结构 ? 43.02M

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    # Context This data set is focused on WLAN fingerprint positioning technologies and methodologies (also know as WiFi Fingerprinting). It was the official database used in the IPIN2015 competition. 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. The UJIIndoorLoc database covers three buildings of Universitat Jaume I ([][1]) 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. 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. # Content - Attributes 001 to 520 (WAP001-WAP520): Intensity value for WAP001. Negative integer values from -104 to 0 and +100. Positive value 100 used if WAP001 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. # Relevent Paper More information can be found in this paper: Joaquín Torres-Sospedra, Raúl Montoliu, Adolfo Martínez-Usó, Tomar J. Arnau, Joan P. Avariento, Mauri Benedito-Bordonau, Joaquín Huerta. UJIIndoorLoc: A New Multi-building and Multi-floor Database for WLAN Fingerprint-based Indoor Localization Problems. In Proceedings of the Fifth International Conference on Indoor Positioning and Indoor Navigation, 2014. Available at: [][2] If your are going to use this dataset in your research, please cite this paper # Acknowledgements The dataset was created by: Joaquín Torres-Sospedra, Raul Montoliu, Adolfo Martínez-Usó, Tomar J. Arnau, Joan P. Avariento, Mauri Benedito-Bordonau, Joaquín Huerta, Yasmina Andreu, óscar Belmonte, Vicent Castelló, Irene Garcia-Martí, Diego Gargallo, Carlos Gonzalez, Nadal Francisco, Josep López, Ruben Martínez, Roberto Mediero, Javier Ortells, Nacho Piqueras, Ianisse Quizán, David Rambla, Luis E. Rodríguez, Eva Salvador Balaguer, Ana Sanchís, Carlos Serra, and Sergi Trilles. # Inspiration The objective is to estimate the building, floor and coordinates (latitude and longitude) of the 1111 samples included in the validation set. Since the real values of the building, floor and coordinates are also included, it is posible to determine the localization error. The formula used in the IPIN2015 competition was the mean of the localization error of each sample. The localization error of each sample can be estimated as follows: Error = building_penality * building_error + floor_penality * floor_error + coordinates_error where: - building_error is 1 if the estimated building is not equal to the real one. 0 otherwise - floor_error is 1 if the estimated floor is not equal to the real one. 0 otherwise - coordinates_error is sqrt( (estimated_latitude - real_latitude)^2 + (estimated_longitude-real_longitude)^2) In the IPIN2015 competition building_penalty and floor_penalty where set to 50 and 4 meters, respectively. [1]: [2]:



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