These contour lines were derived and delivered for Pennsylvania from the PAMAP Quality Level 3 (QL3) LIDAR data collection between 2006 and 2008. Some post-processing has been done to the original deliverables, including merging, line smoothing, and eliminating duplicate (overlapping) data between collections. This dataset renders the contour lines with the following scale-dependent visibility: 100 foot increments between 1:200,000 and 1:100,000 | 50 foot increments between 1:100,000 and 1:30,000 | 20 foot increments between 1:30,000 and 1:5,000 | 10 foot increments between 1:5,000 and 1:1,000 | and 2 foot increments between 1:1,000 and 1:10. The lines have been smoothed using the ArcGIS Pro 3.3 Smooth Line geoprocessing tool via the Polynomial Approximation with Exponential Kernal (PAEK) and setting a 10 ft smoothing tolerance distance. The extent of this data extends slightly beyond the Pennsylvania boundary into all surrounding states to ensure complete coverage of Pennsylvania. Duplicate (overlapping) contour data between collection years and north/south state plane zones has been eliminated by splitting the data from adjacent collects at county boundaries to ensure a seamless product with no duplication or overlapping data. The contour line geometries along the county boundaries that separate different years of PAMAP data collection (2006, 2007, and 2008) do not always connect properly.

Digital Elevation Model (DEM) dataset current as of 2006. This dataset, produced by the PAMAP Program, consists of a raster digital elevation model with a horizontal ground resolution of 3.2 feet. The model was constructed from PAMAP LiDAR (Light Detection and Ranging) elevation points. PAMAP data a.

This dataset consists of classified LiDAR (Light Detection and Ranging) elevation points produced by the PAMAP Program. PAMAP data are organized into blocks, which do not have gaps or overlaps, that represent 10,000 feet by 10,000 feet on the ground. The coordinate system for blocks in the northern half of the state is Pennsylvania State Plane North (datum:NAD83, units: feet); blocks in the sou...

This dataset consists of hydrography (waterbodies) aggregated by the PAMAP Program from data supplied by various Pennsylvania county governments. Additional information is available at the PAMAP website: www.dcnr.state.pa.us/topogeo/pamap.

This data is hosted at, and may be downloaded or accessed from PASDA, the Pennsylvania Spatial Data Access Geospatial Data Clearinghouse http://www.pasda.psu.edu/uci/DataSummary.aspx?dataset=6

The 2005 land cover for Pennsylvania was created through a mix of interpretation of remotely sensed data and use of ancillary data sources. The date actually is a mid-point as the remotely sensed and ancillary data are representative of the time period 2003-2007.

The coding is based on the Anderson Land Use/Land Cover system, where the more descriptive detail in the category is reflected by a higher code value. Further the coding is hierarchical so that each group can be related to other codes within a general category. For example, in the Anderson system the general classification of forest is a 4, a deciduous forest is 41, and so on. For a description of the Anderson system see;

http://landcover.usgs.gov/pdf/anderson.pdf

This project was funded by The PA Department of Conservation and Natural Resources (DCNR)

This data is hosted at, and may be downloaded or accessed from PASDA, the Pennsylvania Spatial Data Access Geospatial Data Clearinghouse http://www.pasda.psu.edu/uci/DataSummary.aspx?dataset=1100

Datasets available at UCI Machine Learning Repository and other repositories. List of datasets used in the experiment with their sources. ForestCover dataset @ https://archive.ics.uci.edu/ml/datasets/Covertype KDD Cup99 dataset @ https://archive.ics.uci.edu/ml/datasets/KDD+Cup+1999+Data PAMAP dataset @ https://archive.ics.uci.edu/ml/datasets/PAMAP2+Physical+Activity+Monitoring Powersupply @ http://www.cse.fau.edu/~xqzhu/stream.html SEA @ http://www.liaad.up.pt/kdus/products/datasets-for-concept-drift Syn002 & Syn003 (generated) @ http://moa.cms.waikato.ac.nz/details/classification/streams/ MNIST @ https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html News20 @ https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html

This directory contains the cross-position activity recognition datasets used in the following paper. Please consider citing this article if you want to use the datasets.

Jindong Wang, Yiqiang Chen, Lisha Hu, Xiaohui Peng, and Philip S. Yu. Stratified Transfer Learning for Cross-domain Activity Recognition. 2018 IEEE International Conference on Pervasive Computing and Communications (PerCom).

These datasets are secondly constructed based on three public datasets: OPPORTUNITY (opp) [1], PAMAP2 (pamap2) [2], and UCI DSADS (dsads) [3].

Here are some useful information about this directory. Please feel free to contact jindongwang@outlook.com for more information.

1. This is NOT the raw data, since I have performed feature extraction and normalized the features into [-1,1]. The code for feature extraction can be found in here: https://github.com/jindongwang/activityrecognition/tree/master/code. Currently, there are 27 features for a single sensor. There are 81 features for a body part. More information can be found in above PerCom-18 paper.

2. There are 4 .mat files corresponding to each dataset: dsads.mat for UCI DSADS, opp_hl.mat and opp_ll.mat for OPPORTUNITY, and pamap.mat for PAMAP2. Note that opp_hl and opp_loco denotes 'high-level' and 'locomotion' activities, respectively. (1) dsads.mat: 9120 * 408. Columns 1~405 are features, listed in the order of 'Torso', 'Right Arm', 'Left Arm', 'Right Leg', and 'Left Leg'. Each position contains 81 columns of features. Columns 406~408 are labels. Column 406 is the activity sequence indicating the executing of activities (usually not used in experiments). Column 407 is the activity label (1~19). Column 408 denotes the person (1~8). (2) opp_hl.mat and opp_loco.mat: Same as dsads.mat. But they contain more body parts: 'Back', 'Right Upper Arm', 'Right Lower Arm', 'Left Upper Arm', 'Left Lower Arm', 'Right Shoe (Foot)', and 'Left Shoe (Foot)'. Of course we did not use the data of both shoes in our paper. Column 460 is the activity label (please refer to OPPORTUNITY dataset to see the meaning of those activities). Column 461 is the activity drill (also check the dataset information). Column 462 denotes the person (1~4). (3) pamap.mat: 7312 * 245. Columns 1~243 are features, listed in the order of 'Wrist', 'Chest', and 'Ankle'. Column 244 is the activity label. Column 245 denotes the person (1~9).

3. There are another 3 datasets with the prefix 'cross_', containing only 4 common classes of each dataset. This is for experimenting the cross-dataset activity recognition (see our PerCom-18 paper). The 4 common classes are lying, standing, walking, and sitting. (1) cross_dsads.mat: 1920*406. Columns 1~405 are features. Column 406 is labels. (2) cross_opp.mat: 5022*460. Columns 1~459 are features. Column 460 is labels. (3) cross_pamap.mat: 3063 * 244. Columns 1~243 are features. Column 244 is labels.

-------- Original references for the 3 datasets:

[1] R. Chavarriaga, H. Sagha, A. Calatroni, S. T. Digumarti, G. Troster, ¨ J. d. R. Millan, and D. Roggen, “The opportunity challenge: A bench- ´ mark database for on-body sensor-based activity recognition,” Pattern Recognition Letters, vol. 34, no. 15, pp. 2033–2042, 2013.

[2] A. Reiss and D. Stricker, “Introducing a new benchmarked dataset for activity monitoring,” in Wearable Computers (ISWC), 2012 16th International Symposium on. IEEE, 2012, pp. 108–109.

[3] B. Barshan and M. C. Yuksek, “Recognizing daily and sports activities ¨ in two open source machine learning environments using body-worn sensor units,” The Computer Journal, vol. 57, no. 11, pp. 1649–1667, 2014.

This dataset provides information about the number of properties, residents, and average property values for 23rd Avenue cross streets in Pampa, TX.

This dataset provides information about the number of properties, residents, and average property values for Cuyler Street cross streets in Pampa, TX.

This dataset provides information about the number of properties, residents, and average property values for Coffee Street cross streets in Pampa, TX.

This dataset provides information about the number of properties, residents, and average property values for Berry Drive cross streets in Pampa, TX.

This dataset provides information about the number of properties, residents, and average property values for County Road 2 1/2 cross streets in Pampa, TX.

Pampa Energia Acción - Los valores actuales, los datos históricos, las previsiones, estadísticas, gráficas y calendario económico - Jun 2025.Data for Pampa Energia | Acción including historical, tables and charts were last updated by Trading Economics this last June in 2025.

2005 PAMAP Hillshade model, derived from LiDAR DEM.

An orthoimage is remotely sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. For this dataset, the natural color orthoimages were produced at 2-feet pixel resolution. The design accuracy is estimated not to exceed 4.8 feet at the 95% confidence level. Each orthoimage provides imagery for a 10,000 by 10,000 feet block on the ground. The projected coordinate system is Pennsylvania State Plane with a NAD83 datum. There is no image overlap been adjacent files. The ortho image filenames were derived from the northwest corner of each ortho tile using the first four digits of the northing and easting coordinates referenced to the Pennsylvania State Plane coordinate system, followed by the State designator "PA", and the State Plane zone designator "S".

Pampa Energia 대출 자본 - 현재 값, 이력 데이터, 예측, 통계, 차트 및 경제 달력 - Jun 2025.Data for Pampa Energia | 대출 자본 including historical, tables and charts were last updated by Trading Economics this last June in 2025.

This dataset, produced by the PAMAP Program, consists of an orthorectified digital raster image (i.e. orthoimage) with a horizontal ground resolution of 1 foot. An orthoimage is a remotely sensed image that has been positionally corrected for camera lens distortion, vertical displacement, and variations in aircraft altitude and orientation. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. Source images were captured in natural color at a negative scale of 1:19200. PAMAP data are organized into blocks, which do not have gaps or overlaps, that represent 10,000 feet by 10,000 feet on the ground. The coordinate system for blocks in the northern half of the state is Pennsylvania State Plane North (datum:NAD83, units: feet); blocks in the southern half of the state are in Pennsylvania State Plane South. A block name is formed by concatenating the first four digits of the State Plane northing and easting defining the block's northwest corner, the State identifier "PA", and the State Plane zone designator "N" or "S" (e.g. 45001210PAS).

2 foot contours that were derived from the LiDAR (Light Detection and Ranging) captured during the PA DCNR (Pennsylvania Department of Conservation and Natural Resources) PAMAP program flyover of Crawford County in 2005. The contours include Index Contours, Index Depression Contours, Intermediate Contours, and Intermediate Depression Contours

2005 PAMAP Hillshade model, derived from LiDAR used for basemap. Further details can be found in the item description.

Pampa Energia Satış - Akım değerleri, tarihsel veriler, tahminler, istatistikler, grafikler ve ekonomik takvim - May 2025.Data for Pampa Energia | Satış including historical, tables and charts were last updated by Trading Economics this last May in 2025.