The automatic detection of different trucks in orthophotos with ArcGIS-Pro<\/h1><\/span><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/i><\/i><\/span>Project Goal<\/span><\/a><\/h4><\/div>\nThe aim of this project was to be able to recognise different trucks on the basis of orthophotos (raster images). For this purpose, the trucks had to be classified into the following classes according to their length.<\/div>\n<\/div><\/div><\/div><\/i><\/i><\/span>Dataset used<\/span><\/a><\/h4><\/div>\nDigital orthophotos of North Rhine-Westphalia from OpenGeodata.NRW.de were used for this project. These images were 4-band images (red, green, blue, infrared) with a resolution of 10cm and an image size of 1000m \u00d7 1000m (10000pixel \u00d7 10000pixel). The NRW orthophotos were divided into 100m \u00d7 100m images and 2400 were selected from them. Of this number, 2148 were labelled for training and validation of the truck detection model.<\/div>\n<\/div><\/div><\/div><\/i><\/i><\/span>Challenges<\/span><\/a><\/h4><\/div>\nModel training requires a large amount of training data to achieve optimal results. For better results and analysis, more training data and better data resolution would have been necessary. For example, the model occasionally recognised cars with trailers (and in rare cases house walls) as trucks. Furthermore, it was difficult to differentiate between two vehicles parked close to each other, as they can be perceived as one vehicle.<\/p>\n
![](\"https:\/\/supperundsupper.com\/wp-content\/uploads\/2021\/03\/gelabelte-Fahrzeuge-geo-ai-min.png\")
<\/a><\/span><\/div>\n<\/div><\/div><\/div><\/i><\/i><\/span>Applied Methods (Implementation)<\/span><\/a><\/h4><\/div>\nA deep learning model was trained for automated recognition and differentiation of the different vehicle types. A pre-trained TernausNet-16 model was used for this purpose. In the case of vehicle detection and classification, the output of the model consists of pixels belonging to the classes background, trailer and cabin. The detected trucks were then divided into 3 classes based on the length of the truck. Subsequently, the trained model and truck classification was implemented in ArcGIS Pro through an inference function and a specific esri model definition file.<\/p>\n<\/div><\/div><\/div>
<\/i><\/i><\/span>Project outcome <\/span><\/a><\/h4><\/div>\nThe model resulted overall with good and reliable accuracy, precision and recall. Over 99% of the trucks were recognised by the model. Less than 10% of the detected trucks were classified in the wrong class. Further fine-tuning of the model, with additional training data from different conditions, can make the model even more robust.<\/p>\n
<\/i><\/i><\/span>Project Goal<\/span><\/a><\/h4><\/div>\nThe aim of this project was to be able to recognise different trucks on the basis of orthophotos (raster images). For this purpose, the trucks had to be classified into the following classes according to their length.<\/div>\n<\/div><\/div><\/div><\/i><\/i><\/span>Dataset used<\/span><\/a><\/h4><\/div>\nDigital orthophotos of North Rhine-Westphalia from OpenGeodata.NRW.de were used for this project. These images were 4-band images (red, green, blue, infrared) with a resolution of 10cm and an image size of 1000m \u00d7 1000m (10000pixel \u00d7 10000pixel). The NRW orthophotos were divided into 100m \u00d7 100m images and 2400 were selected from them. Of this number, 2148 were labelled for training and validation of the truck detection model.<\/div>\n<\/div><\/div><\/div><\/i><\/i><\/span>Challenges<\/span><\/a><\/h4><\/div>\nModel training requires a large amount of training data to achieve optimal results. For better results and analysis, more training data and better data resolution would have been necessary. For example, the model occasionally recognised cars with trailers (and in rare cases house walls) as trucks. Furthermore, it was difficult to differentiate between two vehicles parked close to each other, as they can be perceived as one vehicle.<\/p>\n
<\/a><\/span><\/div>\n<\/div><\/div><\/div><\/i><\/i><\/span>Applied Methods (Implementation)<\/span><\/a><\/h4><\/div>\nA deep learning model was trained for automated recognition and differentiation of the different vehicle types. A pre-trained TernausNet-16 model was used for this purpose. In the case of vehicle detection and classification, the output of the model consists of pixels belonging to the classes background, trailer and cabin. The detected trucks were then divided into 3 classes based on the length of the truck. Subsequently, the trained model and truck classification was implemented in ArcGIS Pro through an inference function and a specific esri model definition file.<\/p>\n<\/div><\/div><\/div>
<\/i><\/i><\/span>Project outcome <\/span><\/a><\/h4><\/div>\nThe model resulted overall with good and reliable accuracy, precision and recall. Over 99% of the trucks were recognised by the model. Less than 10% of the detected trucks were classified in the wrong class. Further fine-tuning of the model, with additional training data from different conditions, can make the model even more robust.<\/p>\n
<\/i><\/i><\/span>Dataset used<\/span><\/a><\/h4><\/div>\nDigital orthophotos of North Rhine-Westphalia from OpenGeodata.NRW.de were used for this project. These images were 4-band images (red, green, blue, infrared) with a resolution of 10cm and an image size of 1000m \u00d7 1000m (10000pixel \u00d7 10000pixel). The NRW orthophotos were divided into 100m \u00d7 100m images and 2400 were selected from them. Of this number, 2148 were labelled for training and validation of the truck detection model.<\/div>\n<\/div><\/div><\/div><\/i><\/i><\/span>Challenges<\/span><\/a><\/h4><\/div>\nModel training requires a large amount of training data to achieve optimal results. For better results and analysis, more training data and better data resolution would have been necessary. For example, the model occasionally recognised cars with trailers (and in rare cases house walls) as trucks. Furthermore, it was difficult to differentiate between two vehicles parked close to each other, as they can be perceived as one vehicle.<\/p>\n
<\/a><\/span><\/div>\n<\/div><\/div><\/div><\/i><\/i><\/span>Applied Methods (Implementation)<\/span><\/a><\/h4><\/div>\nA deep learning model was trained for automated recognition and differentiation of the different vehicle types. A pre-trained TernausNet-16 model was used for this purpose. In the case of vehicle detection and classification, the output of the model consists of pixels belonging to the classes background, trailer and cabin. The detected trucks were then divided into 3 classes based on the length of the truck. Subsequently, the trained model and truck classification was implemented in ArcGIS Pro through an inference function and a specific esri model definition file.<\/p>\n<\/div><\/div><\/div>
<\/i><\/i><\/span>Project outcome <\/span><\/a><\/h4><\/div>\nThe model resulted overall with good and reliable accuracy, precision and recall. Over 99% of the trucks were recognised by the model. Less than 10% of the detected trucks were classified in the wrong class. Further fine-tuning of the model, with additional training data from different conditions, can make the model even more robust.<\/p>\n
<\/i><\/i><\/span>Challenges<\/span><\/a><\/h4><\/div>\nModel training requires a large amount of training data to achieve optimal results. For better results and analysis, more training data and better data resolution would have been necessary. For example, the model occasionally recognised cars with trailers (and in rare cases house walls) as trucks. Furthermore, it was difficult to differentiate between two vehicles parked close to each other, as they can be perceived as one vehicle.<\/p>\n
<\/a><\/span><\/div>\n<\/div><\/div><\/div><\/i><\/i><\/span>Applied Methods (Implementation)<\/span><\/a><\/h4><\/div>\nA deep learning model was trained for automated recognition and differentiation of the different vehicle types. A pre-trained TernausNet-16 model was used for this purpose. In the case of vehicle detection and classification, the output of the model consists of pixels belonging to the classes background, trailer and cabin. The detected trucks were then divided into 3 classes based on the length of the truck. Subsequently, the trained model and truck classification was implemented in ArcGIS Pro through an inference function and a specific esri model definition file.<\/p>\n<\/div><\/div><\/div>
<\/i><\/i><\/span>Project outcome <\/span><\/a><\/h4><\/div>\nThe model resulted overall with good and reliable accuracy, precision and recall. Over 99% of the trucks were recognised by the model. Less than 10% of the detected trucks were classified in the wrong class. Further fine-tuning of the model, with additional training data from different conditions, can make the model even more robust.<\/p>\n
Model training requires a large amount of training data to achieve optimal results. For better results and analysis, more training data and better data resolution would have been necessary. For example, the model occasionally recognised cars with trailers (and in rare cases house walls) as trucks. Furthermore, it was difficult to differentiate between two vehicles parked close to each other, as they can be perceived as one vehicle.<\/p>\n
<\/a><\/span><\/div>\n<\/div><\/div><\/div><\/i><\/i><\/span>Applied Methods (Implementation)<\/span><\/a><\/h4><\/div>\nA deep learning model was trained for automated recognition and differentiation of the different vehicle types. A pre-trained TernausNet-16 model was used for this purpose. In the case of vehicle detection and classification, the output of the model consists of pixels belonging to the classes background, trailer and cabin. The detected trucks were then divided into 3 classes based on the length of the truck. Subsequently, the trained model and truck classification was implemented in ArcGIS Pro through an inference function and a specific esri model definition file.<\/p>\n<\/div><\/div><\/div>
<\/i><\/i><\/span>Project outcome <\/span><\/a><\/h4><\/div>\nThe model resulted overall with good and reliable accuracy, precision and recall. Over 99% of the trucks were recognised by the model. Less than 10% of the detected trucks were classified in the wrong class. Further fine-tuning of the model, with additional training data from different conditions, can make the model even more robust.<\/p>\n
A deep learning model was trained for automated recognition and differentiation of the different vehicle types. A pre-trained TernausNet-16 model was used for this purpose. In the case of vehicle detection and classification, the output of the model consists of pixels belonging to the classes background, trailer and cabin. The detected trucks were then divided into 3 classes based on the length of the truck. Subsequently, the trained model and truck classification was implemented in ArcGIS Pro through an inference function and a specific esri model definition file.<\/p>\n<\/div><\/div><\/div>
<\/i><\/i><\/span>Project outcome <\/span><\/a><\/h4><\/div>\nThe model resulted overall with good and reliable accuracy, precision and recall. Over 99% of the trucks were recognised by the model. Less than 10% of the detected trucks were classified in the wrong class. Further fine-tuning of the model, with additional training data from different conditions, can make the model even more robust.<\/p>\n
The model resulted overall with good and reliable accuracy, precision and recall. Over 99% of the trucks were recognised by the model. Less than 10% of the detected trucks were classified in the wrong class. Further fine-tuning of the model, with additional training data from different conditions, can make the model even more robust.<\/p>\n
![](\"https:\/\/supperundsupper.com\/wp-content\/uploads\/2021\/03\/ergebnisse-Truck-Tracking-GEO-AI-min.png\"){kind=tracking}
<\/a><\/span><\/div>\n<\/div><\/div><\/div><\/div><\/div>