Airports of Pakistan

Polling Station of Pakistan compiled by ALHASAN SYSTEMS Private Limited as one of its R&D projects and in the public interest. ALHASAN SYSTEMS made these Polling Stationpublic before Pakistan’s General Election 2018in its Open Data/ Open Access [OD/OA] Seminar on May 5, 2013. This data is used thoroughly in Pakistan by many stakeholders and researchers including UN and other donor agencies. For further details on the use of this data please download Alhasan Systems monthly Election Bulletins from [http://www.alhasan.com/bulletins/election]. ALHASAN SYSTEMS is a privately owned development company with a history that goes back to 1990. At present is registered in Pakistan and Canada. This hi-tech knowledge management, business psychology modeling, and publishing company is constantly contributing its data and services to both humanitarian and developmental causes through its Public/ Private Partnership [PPP] SKIM and ODOA initiatives. ALHASAN SYSTEMS strives to provide the most cost effective solutions and services, which not only serve its clients’ immediate requirements but also contribute to the much larger cause of community welfare and development. Its area of professional services spreads from environment, energy, health, natural resources, critical infrastructure, utilities management, tourism, and investments, to community development and crisis management. ALHASAN SYSTEMS corporate roadmap focuses on new trends in the field of Geomatics Engineering, Geo-engineering, Data Management, Bio Interfacing, Business Psychology Modeling, Hi-tech publishing, e-Learning, and Smart Power Gridding and Engineering Services. This is possible when fairly serious ecological, political, and moral ramifications are addressed strategically. That’s why social awareness, advocacy, and capacity building remain at the heart of ALHASAN SYSTEMS. ALHASAN SYSTEMS constantly update its data in relation to its projects and also as a service to larger public bodies as well as research community to promote its pioneering 100% self-financed Open Data/ Open Access [OD/OA] initiative in Pakistan. We also share a number of additional layers and thousands of maps each year free-of-cost in the larger public interest. All our data are made available through Pakistan’s only Metadata portal [www.geopakistan.pk] launched in 2012 by iMMAP in collaboration with its partners under USAID funding. This portal is now hosted and maintained at NED University of Engineering & Technology with hundreds of registered researchers in Pakistan. Alhasan Systems is also a United Nations Humanitarian Data Exchange Portal [https://data.hdx.rwlabs.org/] member agency and share its resources through HDX for larger humanitarian benefits.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM), distinguishing between **paved** and **unpaved** roads across the region. Surface classification is derived using a hybrid deep learning approach. For methodological details, see: http://arxiv.org/abs/2410.19874 Recently, we also published a new country-level dataset based on PlanetScope satellite imagery with near-complete coverage of all arterial roads. It is available on HDX under the name Pakistan **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.652 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0758 million km (11.6191%) - Unpaved roads: 0.0598 million km (9.1793%) About 0.5164 million km (79.2016%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0048 million km (0.9208%) of the missing surface information. Intended Use This dataset supports transportation planning, infrastructure analysis, climate emissions assessment, and GIS applications. AI Features - **pred_class**: Model-predicted surface class ("paved" or "unpaved") - **pred_label**: Binary label (0 = paved, 1 = unpaved) - **osm_surface_class**: Surface classification derived from OSM tags - **combined_surface_osm_priority**: Combined classification prioritizing OSM - **combined_surface_DL_priority**: Combined classification prioritizing DL prediction - **n_of_predictions_used**: Number of predictions used for estimation - **predicted_length**: Predicted length based on DL estimation (meters) - **DL_mean_timestamp**: Mean timestamp of predictions used OSM Attributes - **name** - **name:en** - **name:* (local language)** - **highway** - **surface** - **smoothness** - **width** - **lanes** - **oneway** - **bridge** - **layer** - **source** Urban Classification Attributes - **continent** - **country_iso_a2** - **urban** (0 = rural, 1 = urban) - **urban_area** - **osm_id** - **osm_type** Data Sources The dataset originates from OpenStreetMap (OSM) and is augmented with deep learning predictions based on Mapillary street-level imagery, combined with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS 2020 urban layer. More HeiGIT datasets: https://data.humdata.org/organization/heidelberg-institute-for-geoinformation-technology More information: https://heigit.org/ Contact: communications@heigit.org

Road network across the area tagged `highway=*`: motorways, residential streets, service roads, footways, and trails. Useful for routing, accessibility analysis, and evacuation planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: highway - Part of the country exports on HDX by HOT

Railway lines and stations tagged `railway=*`: rail lines, tram tracks, and stations. Useful for transport accessibility analysis and basemap context. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: railway - Part of the country exports on HDX by HOT

Sea ports, harbours and ferry terminals. Useful for maritime logistics, supply-chain analysis, and coastal access planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: industrial=port, harbour, port, amenity=ferry_terminal, building=ferry_terminal - Part of the country exports on HDX by HOT

Accessibility to major cities dataset is modelled as raster-based travel time/cost analysis, computed for the 26 largest cities (>250k habitants) in the country. The following cities are included: City - Population Chiniot 278,528 Nawabshah 279,338 Mingora 331,377 Okara 358,146 Kasur 358,296 Mardan 359,024 Wah Cantonment 379,534 Sahiwal 388,795 Gujrat 390,758 Dera Ghazi Khan 397,362 Rahimyar Khan 420,963 Sheikhūpura 473,269 Larkana 488,006 Sukkur 500,401 Sialkot 656,730 Sargodha 658,208 Bahawalpur 762,774 Quetta 999,385 Hyderabad 1,733,622 Multan 1,872,641 Peshawar 1,969,823 Gujranwala 2,028,421 Rawalpindi Islamabad 3,106,827 Faisalabad 3,210,158 Lahore 11,119,985 Karachi 14,884,402 This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (or optimal location). **Data publication:** 2021-10-18 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@fao.org) **Data lineage:** lineage statement Produced using OpenStreetMap data for roads, railways, rivers; UN Map country border; The HydroSHEDS 15' resolution GRID for the DEM, GHSL - Global Human Settlement Layer. For the city population **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped raster TIF file for Accessibility: Travel Time-Cost to Major Cities (Pakistan - ~ 500m)