Geo-tagged Photos Research Articles

Understanding urban perception with visual data: A systematic review

Visual characteristics of the built environment affect how people perceive and experience cities. For a long time, many studies have examined visual perception in cities. Such efforts have accelerated in recent years due to advancements in technologies and the proliferation of relevant data (e.g., street view imagery, geo-tagged photos, videos, virtual reality, and aerial imagery). There has not been a comprehensive systematic review paper on this topic to reveal an overarching set of research trends, limitations, and future research opportunities. Such omission is plausibly due to the difficulty in reviewing a large number of relevant papers on this popular topic. In this study, we utilized machine learning techniques (i.e., natural language processing and large language models) to semi-automate the review process and reviewed 393 relevant papers. Through the review, we found that these papers can be categorized into the physical aspects of cities: greenery and water, street design, building design, landscape, public space, and the city as a whole. We also revealed that many studies conducted quantitative analyses with a recent trend of increasingly utilizing big data and advanced technologies, such as combinations of street view imagery and deep learning models. Limitations and research gaps were also identified as follows: (1) a limited scope in terms of study areas, sample size, and attributes; (2) low quality of subjective and visual data; and (3) the need for more controlled and sophisticated methods to infer more closely examined impacts of visual features on human perceptions. We suggest that future studies utilize and contribute to open data and take advantage of existing data and technologies to examine the causality of visual features on human perception. The approach developed to accelerate this review proved to be accurate, efficient, and insightful. Considering its novelty, we also describe it to enable replications in the future.

Recreation and its synergies and trade-offs with other ecosystem services of Alpine and pre-Alpine grasslands

Alpine and pre-Alpine grasslands provide numerous ecosystem services including provisioning services (e.g. fodder production), regulating services (e.g. soil erosion reduction), and cultural services (e.g. recreation). While intensively managed grasslands specifically target the production of fodder, more extensively used grasslands are known for being hotspots of biodiversity. However, there is a need to better understand the relationship among the supply of ecosystem services, specifically regarding the use of grasslands for cultural ecosystem services such as recreation. In this study, we investigated the synergies and trade-offs of ecosystem services and analyzed underlying variables related to the recreational use of grasslands. We investigated the supply of recreation (indicated by Photo-User-Days from geo-tagged photos on grasslands), fodder production (indicated by yield), and regulating and habitat ecosystem services (indicated by agri-environmental payments), and analyzed their relationship to management-related variables with a Redundancy Analysis. To better explain the recreational use of grasslands, we further analyzed how environmental and infrastructural features influence the occurrence of Photo-User-Days with a hurdle regression. Finally, we conducted spatial analyses to understand the distribution of Photo-User-Days in space. We found a weak but significant negative relationship between Photo-User-Days and yield, which implies that people slightly prefer extensive grassland to intensive grassland for recreation. Our results also show that agri-environmental schemes targeted towards extensive grassland management can positively influence the recreational use. Other factors, such as proximity to touristic features (e.g. castles), presence of infrastructural features (e.g. cable cars), and environmental characteristics (e.g. low share of croplands, distance to forests), also influenced the spatial distribution of photos on grasslands. The importance of these factors underscores the value of grasslands as a component of the cultural landscape for recreational purposes. These results also suggest that cultural ecosystem services of grasslands can be considered to be co-produced by natural, social, and infrastructural components. The study further discusses limitations to the explanatory power of geo-tagged photo analysis to determine the wide range of cultural ecosystem services of grasslands. We conclude that grasslands play an important role for recreation in (pre-)Alpine landscapes, which can also be effectively supported through targeted agri-environmental payments.

Exploring Tourist Flow Patterns through Geotagged Social Media Data: A Case Study from Sri Lanka

Tourism studies often rely on conventional methods such as interviews and site-specific surveys to collect data on tourist behaviour, mobility patterns, and preferences. However, these methods can be expensive, time-consuming, and limited in scope. Additionally, accurately tracing tourist travel paths can be challenging due to recall bias. Recently, user generated content on social media platforms have emerged as an alternative data source in tourism studies. In this study, we used geotagged posts on Flickr to understand the dominant paths taken by inbound tourists in Sri Lanka. The methodology consisted of three steps. First, geotagged photographs were collected from the Flickr API spanning a ten-year period, from 2012 to 2022. These photographs included metadata such as user ID, the timestamp of photo capture, and geo coordinates indicating where the photo was taken. Second, a density-based clustering algorithm was utilized to identify tourist hotspots. Finally, the Markov Chain model was employed to calculate transition probabilities among different attractions, revealing dominant travel routes within the country. Study’s findings indicate that Cultural heritage attractions were the most popular, comprising 55% of all attractions identified by the algorithm, and are particularly popular in districts such as Anuradhapura, Polonnaruwa, Matale, Kandy, and Galle. Nature-based attractions, constituting 37% of the total, were mainly located in Nuwara Eliya and Badulla Districts, as well as Yala National Park in Hambantota District. For coastal tourism, Galle and Matara Districts were the top preferences. The analysis confirms dominant travel patterns between North Central, Central, and Southwest coast regions. Notably, Galle, Kandy, Matale, Matara, Nuwara Eliya, and Colombo attract tourists from a more diverse array of regions compared to other districts in the country, showcasing their significance as tourist hubs. The study findings highlight opportunities in Jaffna for cultural-heritage tourism, the Eastern coast for coastal tourism, and the central highlands for tea tourism. It also emphasizes the need to develop the less-visited natural sites to ease pressure on popular National Parks. This research's significance lies in its contribution to informed decision-making and the sustainable management of tourist destinations. By understanding tourist mobility patterns and identifying popular attractions through social media data, policymakers can effectively manipulate visitor flows and mitigate excessive tourist pressure, while preserving the authenticity and allure of these destinations.
 
 Keywords: Sustainable tourism, Geotagged photographs, Social media, Tourist mobility, Flickr

Does nationality affect landscape perceptions of urban sacred forests? A Japan–China cross‐cultural comparison

AbstractThis study explores the cultural ecosystem services of sacred forests in urban areas, focusing on the Meiji‐Jingu Shrine's forest in Japan. We assessed the forest's sublimity and landscape perceptions among Japanese and Chinese visitors using a mixed‐method approach. Data were gathered from 359 geotagged photographs and a questionnaire completed by 30 participants, evaluating sublimity and psychological aspects. GIS analysis identified scenic hotspots, and statistical methods compared landscape perceptions between the two visitor groups. Text mining analyzed differences in their forest evaluation. Key findings include (1) Cultural differences in perceiving landscape hotspots, (2) insights into the sublime nature of Meiji‐Jingu Shrine's forest, and (3) varied perceptions of forest sublimity between Japanese and Chinese visitors. This cross‐cultural study provides valuable insights for sacred forest conservation.

Crowdsourcing social values data: Flickr and public participation GIS provide different perspectives of ecosystem services in a remote coastal region

Spatial planning and environmental management are expected to adopt participatory processes. However, the needed spatial data on social values of ecosystem services are limited, especially for marine spatial planning for large, remote coastal areas, and the collection of such information can be time and resource intensive. Crowdsourcing techniques are cost efficient sources of social values data, but must meet the information needs of planners and managers with sufficient confidence. We evaluated the reliability of crowdsourced social values data by assessing the agreement between geotagged photos posted to the social media platform Flickr and data from an online public participation GIS (PPGIS) survey conducted to support marine spatial planning in the Kimberley region of Western Australia.Flickr could not represent all of the social values mapped in the PPGIS dataset, was notably dominated by scenic values, and under-represented the importance of recreational fishing and Aboriginal cultural values. The Flickr dataset was more affected by accessibility from settlements and transportation infrastructure (roads, boat ramps) and more restricted spatially. Maximum entropy (maxent) distribution models developed to explore the socioecological drivers determining where social values occur also highlighted that Flickr and PPGIS samples provide different perspectives on social values, even after accounting for differences in sampling intensity. The Flickr dataset suggested that the social values occurred under narrower ranges of socioecological conditions, and the PPGIS dataset was more likely to find protected areas to be valued, likely because participants believe protected areas to support those social values, but may not themselves pursue them there.Flickr and PPGIS crowdsourced datasets provide different perspectives on the spatial distribution of social values for several reasons: They differ in their spatial and demographic biases, as well as their measurement of revealed vs. stated preferences. Flickr is most effective at representing social values that are easily photographed and interpretable in photos, and may not capture some information needs of managers. PPGIS is under greater investigator control but, because stated preferences can be disconnected from actual use of the sites mapped, may be best suited for assessing management preferences and social acceptability. With understanding of the nuances of these datasets, crowdsourced social values data can be applied most appropriately to support successful planning outcomes.

What on-site landscape experiences attract potential visitors to a site? A Japan–Korea cross-cultural comparison

ABSTRACT In the age of big data, social media’s influence on on-site landscape experiences is growing, and the relationship between on- and off-site landscape planning and management is becoming more important. The study’s objectives were: 1) to identify scenic hotspots based on on-site visitor experiences, 2) clarify landscape preferences and attractiveness based on photographs taken at scenic hotspots, and 3) examine similarities and differences between Japanese and Korean visitors in terms of landscape preferences and attractiveness. The data were collected using geotagged visitor employed photography from 153 visitors to the Takao Quasi-National Park in Japan were used to understand off-site appreciation, and compare Japanese and Korean visitors’ experiences. Photo-based questionnaires were completed by 42 Japanese and 40 Korean respondents. We collected 1,645 geotagged photographs, and performed a GIS analysis with network-based kernel density estimation to identify scenic hotspots. The data extracted from the photo-based questionnaire were used to analyze the relationship between on-site and off-site experiences, as well as similarities and differences between the Japanese and Korean participants. The data were analyzed using means, Welch’s t-test, Wilcoxon sum-rank test, Pearson’s correlation analysis, and Kendall’s correlation analysis. The results identified nine scenic hotspots, and revealed that the photographs that the Japanese visitors preferred to take would not necessarily attract tourists, nor were they the same as the scenic spots and objects the Korean visitors found attractive. As international tourism expands, cross-cultural research on on- and off-site experiences and preferences has become increasingly important for forest landscape management and sustainable tourism.

Establishment of Herbal Garden in the Galgotias University Campus - An Ex-situ Conservation Approach

Herbal gardens are a rich source of plant-based medicines and their derivatives. Both in urban and rural locations, herbal plants have an important role in traditional medicine. The development of herbal garden includes propagation, multiplication, research, education, and extension activities as well. In the present study, we took herbal plants from different nurseries of Noida and Greater Noida of Gautam Buddha Nagar district, Uttar Pradesh and developed herbal garden in the Galgotias University campus. The herbal garden has approximately 45 different plants, including shrubs (22), trees (13), herbs (9) and grass (1) etc. The geotagged photographs of each herbal plant were captured using GPS map camera. The ex-situ conservation of herbal garden in Galgotias University would help and motivate faculties and students of the university, to undertake further research on medicinal plants.

Crop Identification Using Deep Learning on LUCAS Crop Cover Photos.

Massive and high-quality in situ data are essential for Earth-observation-based agricultural monitoring. However, field surveying requires considerable organizational effort and money. Using computer vision to recognize crop types on geo-tagged photos could be a game changer allowing for the provision of timely and accurate crop-specific information. This study presents the first use of the largest multi-year set of labelled close-up in situ photos systematically collected across the European Union from the Land Use Cover Area frame Survey (LUCAS). Benefiting from this unique in situ dataset, this study aims to benchmark and test computer vision models to recognize major crops on close-up photos statistically distributed spatially and through time between 2006 and 2018 in a practical agricultural policy relevant context. The methodology makes use of crop calendars from various sources to ascertain the mature stage of the crop, of an extensive paradigm for the hyper-parameterization of MobileNet from random parameter initialization, and of various techniques from information theory in order to carry out more accurate post-processing filtering on results. The work has produced a dataset of 169,460 images of mature crops for the 12 classes, out of which 15,876 were manually selected as representing a clean sample without any foreign objects or unfavorable conditions. The best-performing model achieved a macro F1 (M-F1) of 0.75 on an imbalanced test dataset of 8642 photos. Using metrics from information theory, namely the equivalence reference probability, resulted in an increase of 6%. The most unfavorable conditions for taking such images, across all crop classes, were found to be too early or late in the season. The proposed methodology shows the possibility of using minimal auxiliary data outside the images themselves in order to achieve an M-F1 of 0.82 for labelling between 12 major European crops.

An analysis on the spatiotemporal behavior of inbound tourists in Jiaodong Peninsula based on Flickr geotagged photos

Exploring the spatiotemporal behavior characteristics of inbound tourists is of great practical significance to the management and planning of attractions. Based on Flickr geotagged photos and metadata, the research analyzes the spatiotemporal behavior characteristics of inbound tourists in Jiaodong Peninsula from the perspective of tourist flow network. We decompose the time series characteristics of tourists with STL decomposition algorithm, and predict the inbound tourism trend through the time series model. We propose the Hot Status Index (HSI) based on network centrality, extract and analyze the distribution pattern of inbound tourism hotspots with the structural hole method. The k-core decomposition algorithm is introduced to analyze the hierarchical characteristics of the tourist flow network. And Fast Unfolding algorithm is adopted to analyze the characteristics of community aggregation under different scales, dividing the scenic area into four communities. The results show that the time series model can accurately estimate the trend of the number of tourists. And there is a “competition” effect among attractions in Jiaodong Peninsula. The attractions show a hierarchy effect, of which the core layer has obvious small-world characteristics, with a clustering coefficient of 0.768. The inter-city tourist flow in Jiaodong Peninsula reveals a closed “multi-triangle” distribution pattern, mainly in the marginal coastal cities. Qingdao old town presents community stability, and other urban communities have a scale effect, mainly comprising two tourism circles.

Planning for fear of crime reduction: Assessing the impact of public space regeneration on safety perceptions in deprived neighborhoods

Fear of crime significantly reduces people’s time–space access to and use of public space, notably in high-crime neighborhoods where concerns around personal safety are more acute. One widely used strategy to reduce fear of crime is the regeneration of the built environment. However, tension remains on whether this strategy is effective, and if it is, then where, which and for whom public space interventions work. This research, incorporating a gender perspective, assesses whether neighborhood-level regeneration of public space significantly enhances or reduces residents’ perceptions of safety in deprived urban areas with a gender perspective. To test these impacts, we run a randomized control trial with 100 residents in a high-crime neighborhood in Santiago de Chile. A series of geotagged photographs of the area and ten treatment photo simulations of proposed interventions were rated by residents according to their perceived safety related to crime. The results suggest that: highly unsafe perceptions cluster in specific neighborhood locations and are particularly acute for women; the regeneration of public spaces significantly increases perceived safety for both men and women; and the effectiveness of different interventions is gender-specific. The results suggest, while an effective technique, public space regeneration in deprived neighborhoods can be further optimized through urban design and planning policy that are space- and gender-specific. The technique presented could support researchers and practitioners to understand the spatial distribution of perceptions of safety, to select effective interventions to make deprived neighborhoods feel safer, and to assess the impact of regeneration strategies.

Examining Rail Transportation Route of Crude Oil in the United States Using Crowdsourced Social Media Data

Safety issues associated with transporting crude oil by rail have been a concern since the boom of the U.S. domestic shale oil production in 2012. During the last decade, over 300 crude-oil-by-rail incidents have occurred in the United States. Some of them have caused adverse consequences including fire and hazardous materials leakage. However, only limited information on crude-on-rail routes and their associated risks is available to the public. To this end, this study proposed an unconventional way to reconstruct crude-on-rail routes using geotagged photos harvested from the Flickr website. The proposed method linked the geotagged photos of crude oil trains posted online with national railway networks to identify potential railway segments that those crude oil trains were traveling on. A shortest path-based method was applied to infer the complete crude-on-rail routes, by utilizing the confirmed railway segments as well as their directional information. Validation of the inferred routes was performed using a public map and official crude oil incident data. The results suggested that the inferred routes based on geotagged photos had high coverage, with approximately 96% of the documented crude oil incidents aligned with the reconstructed crude-on-rail network. The inferred crude oil train routes were found to pass through several metropolitan areas of high population density, who were exposed to potential risk. These findings could improve situational awareness for policy makers and transportation planners. In addition, with the inferred routes, this study has established a good foundation for future crude oil train risk-analyses along the rail route.

Spatiotemporal behavior pattern differentiation and preference identification of tourists from the perspective of ecotourism destination based on the tourism digital footprint data.

Tourist impact management in ecotourism destinations requires an accurate description of tourists' spatiotemporal behavior patterns and recreation preferences to minimize the ecological environmental impact and maximize the recreation experience. This study classified tourist behaviors into five typical behavior patterns based on the digital footprints of tourists visiting Ziwuyu of the Qinling Mountains, including 348 traveling tracks and 750 corresponding geotagged photographs: short-distance, traversing, reentrant, large loop, and small loop. Furthermore, each behavior pattern's recreation preference was identified using photograph analysis. Tourists with large-loop and reentrant behavior patterns have 89.8% and 30% chances of visiting Jianshanding, respectively. Key protected areas are faced with the risk of ecological environmental damage. Based on the behavior pattern differentiation and preference of tourists, this paper provides a decision-making basis for the classified management and guidance of tourists in ecotourism destinations. It has reference value for the management of similar ecotourism destinations.

Multi-Level Visual Similarity Based Personalized Tourist Attraction Recommendation Using Geo-Tagged Photos

Geo-tagged photo-based tourist attraction recommendation can discover users’ travel preferences from their taken photos, so as to recommend suitable tourist attractions to them. However, existing visual content-based methods cannot fully exploit the user and tourist attraction information of photos to extract visual features, and do not differentiate the significance of different photos. In this article, we propose multi-level visual similarity-based personalized tourist attraction recommendation using geo-tagged photos (MEAL). MEAL utilizes the visual contents of photos and interaction behavior data to obtain the final embeddings of users and tourist attractions, which are then used to predict the visit probabilities. Specifically, by crossing the user and tourist attraction information of photos, we define four visual similarity levels and introduce a corresponding quintuplet loss to embed the visual contents of photos. In addition, to capture the significance of different photos, we exploit the self-attention mechanism to obtain the visual representations of users and tourist attractions. We conducted experiments on two datasets crawled from Flickr, and the experimental results proved the advantage of this method.

Automatic construction of POI address lists at city streets from geo-tagged photos and web data: a case study of San Jose City

Automatic construction of POI address lists at city streets from geo-tagged photos and web data: a case study of San Jose City

Pull and Push Drivers of Giant-Wave Spectators in Nazaré, Portugal: A Cultural Ecosystem Services Assessment Based on Geo-Tagged Photos

This paper maps the cultural ecosystem services (CES) of a well-known giant-wave hotspot located in Nazaré, Portugal. The paper adopts a qualitative approach combining an auto-ethnographic direct observation of a journey and the content analysis of photos and videos posted on the YouTube and Facebook pages of tourists and operators. A total of 44 geotagged photos from a sample of 6914 photos retrieved from Flickr allowed the classification and spatial distribution of several CES: (1) recreational—surf activities; (2) aesthetic—photography; (3) spiritual—dark tourism and risk recreation; (4) intangible heritage—maritime knowledge; (5) scientific—wave height forecast; (6) sense of place; and (7) social relations. The paper also proposes a theoretical framework that highlights the pull drivers (risk recreation, storm chasing, or spectacular death voyeurism) and the push drivers (e.g., marketing campaigns and wave forecasts alerts) that explain the behaviors of the big-wave spectators/chasers during the experience journey. Public decision-makers, destination marketing organizations, tourism operators, and business entrepreneurs must acknowledge the relevance of journey mapping in order to identify the moments of stress and the touchpoints associated with peak/positive experiences generated by these CES. This study confirms some push and pull factors assessed by previous studies.

Pl@ntNet Crops: merging citizen science observations and structured survey data to improve crop recognition for agri-food-environment applications

We present a new application to recognize 218 species of cultivated crops on geo-tagged photos, ‘Pl@ntNet Crops’. The application and underlying algorithms are developed using more than 750k photos voluntarily collected by Pl@ntNet users. The app is then enriched by data and photos coming from the European Union’s (EU) Land Use and Coverage Area frame Survey (LUCAS). During five tri-annual LUCAS campaigns from 2006 to 2018, 242 476 close-up ‘cover’ photos of crops were collected. The survey protocol for these photos specified that ‘the picture should be taken at a close distance, so that the structure of leaves can be clearly seen, as well as flowers or fruits’. This unique labelled data provides an opportunity to further generalize the Pl@ntNet computer vision algorithms to recognize crops and enlarge their geographic representivity across the EU. To include LUCAS cover photos, we semantically match Pl@ntNet species and LUCAS legends, predict the species on LUCAS cover photos with the existing Pl@ntNet algorithm, and consider the accuracy of the classification and the number of species enriched by the photos. By setting a threshold of 0.5 on the Pl@ntNet prediction probabilities, 70 170 LUCAS photos representing 101 species classified with an accuracy of 0.9 were added to the ‘Crops’ app. The thematic accuracy of the legacy LUCAS data was improved by distinguishing 218 species, opposed to the original 36 LUCAS levels. Official and publicly financed LUCAS datastreams can now be improved because of Pl@ntNet citizen science, photo collection, and deep learning model development. Further use of the app and policy-relevant workflows in the agri-food-environment domain are discussed.

Analyzing national parks visitor activities using geotagged social media photos

Visitor activities within national parks are important information for park management. Currently, national parks and nature-based recreation area worldwide have no systematic visitor activity data collection schemes. Traditional visitor movement or activity data collection methods used in tourism studies are often costly in money and time and difficult to implement. This paper presents a framework and formal procedure that uses geotagged photos crowdsourced from social media platforms to uncover visitor activities within national parks. First, the daily individual space-time paths are constructed from the photo geotags and timestamps for all visitors. Then the following information is derived from the space-time paths: 1) four activity types including stationary, walking, vehicle riding, and flying; 2) the total time of various visitor activities in a park; 3) visitor flow directions aggregated to a grid; 4) a graph constructed from space-time paths and the directed and undirected connections (flows) between POIs; and 5) the centrality of POIs derived from the graph. A case study of four highly visited U.S. national park units from 2008 to 2021 demonstrates the process and capabilities of the method. It also reveals a trend of increased walking time and decreased stationary time in the case study parks over the years. Although this study is only based on Flickr photos, the method can be implemented with data derived from any social media platforms as long as geotags and timestamps are available. The method may be used to monitor visitor activities and movements within national parks and other outdoor recreation areas, especially remote and unmonitored locations.

ReconTraj4Drones: A Framework for the Reconstruction and Semantic Modeling of UAVs’ Trajectories on MovingPandas

Unmanned aerial vehicles (UAVs), also known as drones, are important for several application domains, such as the military, agriculture, cultural heritage documentation, surveillance, and the delivery of goods/products/services. A drone’s trajectory can be enriched with external and heterogeneous data beyond latitude, longitude, and timestamp to create its semantic trajectory, providing meaningful and contextual information on its movement data, enabling decision makers to acquire meaningful and enriched contextual information about the current situation in the field of its operation and eventually supporting simulations and predictions of high-level critical events. In this paper, we present an ontology-based, tool-supported framework for the reconstruction, modeling, and enrichment of drones’ semantic trajectories. This framework extends MovingPandas, a widely used and open-source trajectory analytics and visualization tool. The presented research extends our preliminary work on drones’ semantic trajectories by contributing (a) an updated methodology for the reconstruction of drones’ trajectories from geo-tagged photos taken by drones during their flights in cases in which flight plans and/or real-time movement data have been lost or corrupted; (b) an enrichment of the reconstructed trajectories with external data; (c) the semantic annotation of the enriched trajectories based on a related ontology; and (d) the use of SPARQL queries to analyze and retrieve knowledge related to the flight of a drone and the field of operations (context). An evaluation of the presented framework, namely, ReconTraj4Drones, was conducted against several criteria, using real and open datasets.

Estimating Bounding Box for Point of Interest Using Social Media Geo-Tagged Photos

The accuracy and completeness of information in geographical databases are very important for many location-based applications and services. However, the incompleteness in geographical databases is currently an issue. One consequence of this is that the geographic bounding boxes of many points of interests (POIs) have not been known. This paper studies the problem of estimating geographic bounding boxes for POIs using geo-tagged photos contributed by public users on social media. We present a novel approach using relevant geo-tagged photos of POIs to estimate geographic bounding boxes for the POIs. In the proposed method, we extend to apply survival analysis with random distance variable for our estimation. We demonstrate the superiority and effectiveness of our proposed approach over competing methods.

Monitoring Road maintenance using video-geotagging in geographical information system: an innovative approach

Maintenance of roads is a key concern for smooth flow of traffic and goods for any economy to thrive. The maintenance part has been limited in GIS to information collected through ground-based surveys, GPS location of affected road area, uploading geo-tagged photos shared by public through mobile app on GIS data. The present work focuses on one such innovative approach using geo-tagging of videos of road surface with Road layer in GIS. Stretches of road in and around Ludhiana city of Punjab to reflect different scenarios such as roads located in open village area, highways, and broad and congested city roads have been used for understanding condition of surface of road in a contiguous fashion.