As the global transition towards renewable energy sources accelerates, solar power becomes an increasingly important solution. Identifying and understanding the current distribution of solar panel installations is crucial for future planning and decision-making process. This paper introduces SolarDetector, a transformer-based neural network model, which we developed and fine-tuned for the accurate detection of solar panels. It achieves 91.0% mIoU for the task of masking solar panels on SWISSIMAGE dataset.

Maps provide various sources of information. An important example of such information is textual labels such as cities, neighborhoods, and street names. Although we treat this information as facts, and despite the massive effort done by providers to continuously improve their accuracy, this data is far from perfect. Discrepancies in textual labels rendered on the map are one of the major sources of inconsistencies across map providers. These discrepancies can have significant impacts on the reliability of the derived information and decision-making processes. Thus, it is important to validate the accuracy and consistency in such data. Most providers treat this data as their propriety data and it is not available to the public, thus we cannot compare the data directly. To address these challenges, we introduce a novel computer vision-based approach for automatically extracting and classifying labels based on the visual characteristics of the label, which indicates its category based on the format convention used by the specific map provider. Based on the extracted data, we detect the degree of discrepancies across map providers. We consider three map providers: Bing Maps, Google Maps, and OpenStreetMaps. The neural network we develop classifies the text labels with an accuracy up to 93% in all providers. We leverage our system to analyze randomly selected regions in different markets. The studied markets are USA, Germany, France, and Brazil. Experimental results and statistical analysis reveal the amount of discrepancies across map providers per region. We calculate the Jaccard distance between the extracted text sets for each pair of map providers, which represents the discrepancy percentage. Discrepancies percentages as high as 90% were found in some markets.

We demonstrate MapsVision, a computer vision-based framework capable of identifying discrepancies across different map providers for similar geographical locations. In this study, we primarily focus on three map providers including: (a) Bing Maps, (b) Google Maps, and (c) OpenStreetMap. MapsVision detects textual data discrepancies such as: (1) missing location labels (2) misspelled or different keywords, (3) shifted labels, and (4) level of significance manifested by text or label font-size and color. For a given location, our MapsVision framework compares textual labels based on a ground truth entered manually to those that exist in the three map providers. We then use the results of the textual extraction to determine the accuracy of textual data appearing on map providers. Our framework intelligently identifies the set of techniques for each map providers' that can maximize the overall detection accuracy. MapsVision is composed of three main building blocks including: (a) a capturing module that captures map tiles from map providers, (b) an analysis tool that uses computer vision and text-analytic techniques, and (c) a rich visualization interface for displaying statistical and real-time analytics. The objective of MapsVision is to help map editors improve the textual quality of their maps compared to other map providers.

Today, people's lives are enriched by the integration of electronic maps via smartphones. Electronic maps are required for a variety of commercial activities, such as catering, movie viewing, and tourism. Route planning and navigation are particularly intrinsically linked to electronic maps. As a result, it is critical that the roads on the electronic map are complete and accurate. At the present time, there are discrepancies between the map roads of various providers. This paper evaluates the roads on various map providers' maps. Due to the varied terrain depicted on the map, assessing the road properties can be challenging. Additionally, roads of varying thicknesses exist within a tile image, making it difficult to quantify the map's road lengths. This paper proposes a method for extracting road segments using an image binarization technique and employs edge erosion to assist in automatically computing the length of roads within maps. Throughout the paper, we provide comparison and statistical analysis on using our proposed road length detection model across map providers. Results show that our detection model can identify road length accurately and hence provide an overall measure of quality of maps.

Map service providers are working hard to maintain high-quality maps service to more than three billion digital maps users. As each provider presents its unique routing engine and road network graph (RNG) mapping techniques, inconsistencies in services provided are inevitable. These inconsistencies may be of two types- (1) inconsistencies in RNG, including missing or shifted road segments, missing turn restriction, or mislabeled road attributes; or (2) inconsistencies in routing service arising from the unique routing algorithm (RA). Discovering those inconsistencies would improve the routing services efficiency. This paper presents a system, named GeoDart, that compares publicly available routing data from the APIs of Bing Maps, Google Maps, and OpenStreetMaps (OSM) to automatically discover discrepancies. The system categorizes the detected discrepancies based on (1) routing data such as distance, duration, and route geometry, (2) the attributes of the road segments, and (3) the connectivity and turn restrictions of the RNG. Equipped with an ensemble of Multi-Layer Perception (MLP) and Support Vector Machine Classifiers (SVC), GeoDart can efficiently discover and classify maps discrepancies. Through its graphical interface, the GeoDart system enables users such as professional editors and cartographers to visually inspect, identify, and correct map discrepancies mutually across the three engines.

Routing engines and navigation services are among the top applications that take advantage of the OpenStreetMap (OSM) collaborative project. With that said, it is key for the underlying road network data provided by the OSM public geographic datasets to be as accurate as possible for these services to work correctly. This means that road networks must be fully connected, and constraints such as turn restrictions, road directionality, and correct road classification must be respected. However, being an open-license project with around 7 million users and a daily average of about 3.5 million map changes, errors in the data are far from lacking. Issues like misclassified road segments, incorrect connections and gaps in road networks are fairly common, and they pose a complex yet notable obstacle that jeopardizes the accuracy and reliability of routing services that rely on the OSM data. This paper presents a system named OSMRunner developed to tackle and remedy all sorts of connectivity errors in OSM graphs. The system automatically detects connectivity errors that otherwise require an extensive manual process to discover. It is designed to achieve full connectivity in any area of the OSM road network. User input is made available via a friendly graphical user interface that allows visual error investigation, fix suggestions, and easy access to editing tools.

All online map service providers are working hard to maintain high-quality maps to provide high-quality services. Example inaccuracies that can be encountered in the provided maps may include missing road segments, shifted road segments, missing road connections, missing or incorrect turn restrictions, and mislabeling road attributes like marking a directional road as one-way. Maps may also be rapidly changing in some areas due to new constructions. While the accuracy of various mapping systems, as given by service providers, is known to be high, even the minor discrepancies in the underlying maps may lead to unsatisfactory user experience in routing and location-based services.