Combining Satellite Imagery and Open Data to Map Road Safety

Improving road safety is critical for the sustainable development of cities. A road safety map is a powerful tool that can help prevent future traffic accidents. However, accurate mapping requires accurate data collection, which is both expensive and labor intensive. Satellite imagery is increasingly becoming abundant, higher in resolution and affordable. Given the recent successes deep learning has achieved in the visual recognition field, we are interested in investigating whether it is possible to use deep learning to accurately predict road safety directly from raw satellite imagery. To this end, we propose a deep learning-based mapping approach that leverages open data to learn from raw satellite imagery robust deep models able to predict accurate city-scale road safety maps at an affordable cost. To empirically validate the proposed approach, we trained a deep model on satellite images obtained from over 647 thousand traffic-accident reports collected over a period of four years by the New York city Police Department. The best model predicted road safety from raw satellite imagery with an accuracy of 78%. We also used the New York city model to predict for the city of Denver a city-scale map indicating road safety in three levels. Compared to a map made from three years' worth of data collected by the Denver city Police Department, the map predicted from raw satellite imagery has an accuracy of 73%

Robust Template Matching Using Multiple-Layered Absent Color Indexing

Color is an essential feature in histogram-based matching. This can be extracted as statistical data during the comparison process. Although the applicability of color features in histogram-based techniques has been proven, position information is lacking during the matching process. We present a conceptually simple and effective method called multiple-layered absent color indexing (ABC-ML) for template matching. Apparent and absent color histograms are obtained from the original color histogram, where the absent colors belong to low-frequency or vacant bins. To determine the color range of compared images, we propose a total color space (TCS) that can determine the operating range of the histogram bins. Furthermore, we invert the absent colors to obtain the properties of these colors using threshold hT. Then, we compute the similarity using the intersection. A multiple-layered structure is proposed against the shift issue in histogram-based approaches. Each layer is constructed using the isotonic principle. Thus, absent color indexing and multiple-layered structure are combined to solve the precision problem. Our experiments on real-world images and open data demonstrated that they have produced state-of-the-art results. Moreover, they retained the histogram merits of robustness in cases of deformation and scaling

Robust and Precise Matching Algorithm Combining Absent Color Indexing and Correlation Filter

This paper presents a novel method that absorbs the strong discriminative ability from absent color indexing (ABC) to enhance sensitivity and combines it with a correlation filter (CF) for obtaining a higher precision; this method is named ABC-CF. First, by separating the original color histogram, apparent and absent colors are introduced. Subsequently, an automatic threshold acquisition is proposed using a mean color histogram. Next, a histogram intersection is selected to calculate the similarity. Finally, CF follows them to solve the drift caused by ABC during the matching process. The novel approach proposed in this paper realizes robustness in distortion of target images and higher margins in fundamental matching problems, and then achieves more precise matching in positions. The effectiveness of the proposed approach can be evaluated in the comparative experiments with other representative methods by use of the open data