Extracting road network structure from heatmaps of GPS trajectories using convolutionalneural networks: ableitung des wegenetzes aus GPS-trajektoren-heatmaps unterverwendung von convolutional neural networks
Başlık çevirisi mevcut değil.
- Tez No: 644618
- Danışmanlar: PROF. DR. CLAUS BRENNER, DR. FRANK THİEMANN
- Tez Türü: Yüksek Lisans
- Konular: Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Computer Engineering and Computer Science and Control
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2017
- Dil: İngilizce
- Üniversite: Gottfried Wilhelm Leibniz Universität Hannover
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 117
Özet
Özet yok.
Özet (Çeviri)
Road network extraction from GPS trajectories has always been an overemphasized topic for the researches on map improvement. The existence of huge amount of GPS trajectories collected from vehicle movements, cycling and running activities, has encouraged researchers to work on this topic. Besides that, the heat maps (i.e. density maps) of trajectories have been visualized in order to indicate the frequency of use of road segments. With the work represented in this thesis, extraction of road networks from GPS trajectories has been aimed by proposing the use of convolutional neural networks on heat maps. For this purpose, a supervised learning was executed by feeding the network with two kinds of heat maps and their ground truth labels belonging to the city of Hanover in Germany. One of the heat maps was created by imitating the behavior of real world vehicle trajectories, the other one was retrieved from a website that is based on cycling trajectories. Their ground truth labels were created with regard to OpenStreetMap road network. Additionally, three more heat maps without target datasets were produced in order to spot the differences on robustness of the constructed model. One of them was created for the city of Berlin based on trajectory simulation, the other one was retrieved from a website that is based on cycling trajectories for another part of the city of Hanover, and the last one was created directly from vehicle trajectories for the city of Chicago in the United States. According to the experimental results, the optimum parameters and appropriate optimization method were found out for the constructed model. Results indicate that the quality measures (e.g. precision, recall and F1Score) of road network extraction with the proposed method are dependent on the training and validation datasets delivered to the model. Also, in case of various and large amount of datasets, there might be improvement on learning ability of the constructed model.
Benzer Tezler
- LİDAR verileri ile SAM üretiminde farklı arazi türlerine göre performans araştırması
Performance research according to the different terrain types in SAM production with LİDAR data
NURAY BAŞ
Doktora
Türkçe
2016
Jeodezi ve Fotogrametriİstanbul Teknik ÜniversitesiGeomatik Mühendisliği Ana Bilim Dalı
PROF. DR. HİLAL GONCA COŞKUN
- Orman ürünleri transportu nedeniyle meydana gelen yüzey bozulmalarının fotogrametrik yöntemle değerlendirilmesi
Using photogrammetric approach for assessment of forest road surface deformation due to forest transportation
SEÇKİN ŞİRELİ
Yüksek Lisans
Türkçe
2019
Ormancılık ve Orman MühendisliğiKahramanmaraş Sütçü İmam ÜniversitesiOrman Mühendisliği Ana Bilim Dalı
DR. ÖĞR. ÜYESİ SERCAN GÜLCİ
- Derin öğrenme kullanarak uydu görüntülerinden yol tespiti
Road identification from satellite imagery using deep learning
MOHAMMED MAHMOOD ABDULWAHAB NASSER
Yüksek Lisans
Türkçe
2022
Jeodezi ve FotogrametriErciyes ÜniversitesiHarita Mühendisliği Ana Bilim Dalı
DOÇ. DR. ÜMİT HALUK ATASEVER
- Çok yüksek çözünürlüklü uydu görüntülerinden grafik tabanlı bilgi çıkarımı
Graph-based infortmation extraction from very high resolution satellite images
NURETTİN SİNANOĞLU
Yüksek Lisans
Türkçe
2024
Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrolİstanbul Teknik ÜniversitesiUydu Haberleşmesi ve Uzaktan Algılama Ana Bilim Dalı
PROF. DR. ELİF SERTEL
- Deep learning based road segmentation from multi-source and multi-scale data
Çok kaynaklı ve çok ölçekli veriyle derin öğrenme tabanlı yol bölütlenmesi
OZAN ÖZTÜRK
Doktora
İngilizce
2023
Jeodezi ve Fotogrametriİstanbul Teknik ÜniversitesiGeomatik Mühendisliği Ana Bilim Dalı
PROF. DR. DURSUN ZAFER ŞEKER