Network mapping is used to extract the coordinates of a network's components in an image. Furthermore, machine learning algorithms have demonstrated their efficacy in advancing the field of network mapping across various domains, including mapping of road networks and blood vessel networks. However, accurately mapping of road networks still remains a challenge due to difficulties in identification and separation of roads in the presence of occlusion caused by trees, as well as complex environments, such as parking lots and complex intersections. Additionally, the segmentation of blood vessels networks, such as the ones in the retina, is also not trivial due to their complex shape and thin appearance. Therefore, the aim for this thesis was to investigate two deep learning approaches to improve mapping of networks, namely by refining existing road network probability maps, and by estimating road network orientations. Additionally, the thesis explores the possibility of using a machine learning model trained on road network probability maps to refine retina network segmentations. In the first approach, U-Net models with a binary output channel were implemented to refine existing probability maps of networks. In the second approach, ResNet models with a regression output were implemented to estimate the orientation of roads within a network. The models for refining road network probability maps were evaluated using F1-score and MCC-score, while the models for estimating road network orientation were evaluated based on angle loss, angle difference, F1-score, and MCC-score.  The results for refining road segmentations yielded an increase of 0.102 MCC-score compared to the baseline (0.701). However, when applying the segmentation refinement model to retina images, the output from the model achieved merely 0.226 in MCC-score. Nevertheless, the model demonstrated the capability to identify and refine the segmentation of large blood vessels. Additionally, the estimation of road network orientation achieved an average error of 10.50 degrees. It successfully distinguished roads from the background, achieving an MCC-score of 0.805. In conclusion, this thesis shows that a deep learning-based approach for road segmentation refinement is beneficial, especially in cases where occlusions are present. However, the refinement of retina image segmentations using a model trained on roads and tested on retina images produced unsatisfactory results, likely due to differences in scale between road width and vessel size. Further experiments with adjustments in image scales are likely needed to achieve better results. Moreover, the orientation model demonstrated promising results in estimating the orientation of road pixels and effectively differentiating between road and non-road pixels.

L’époque hellénistique est, pour la péninsule anatolienne, une période d’intensification des circulations. Après la conquête d’Alexandre, elle apparaît en effet pour les Gréco-Macédoniens comme un passage obligé vers l’Orient. Ce travail se propose d’étudier les infrastructures qui permirent ces déplacements : les routes et tous les aménagements qui les bordaient ou assuraient sa continuité ; les fleuves navigables, et les lignes maritimes qui reliaient les ports des côtes micrasiatiques. À partir de différentes études de cas, on montre comment ces voies de circulation fonctionnaient en réseau au niveau local, régional et supra-régional. Dans un second temps, on analyse l’influence des institutions sur l’organisation des circulations : les pouvoirs, par l’installation d’infrastructures, le contrôle des déplacements et diverses politiques économiques et financières, avaient la possibilité d’encourager la circulation des biens et des personnes. Enfin, on s’intéresse aux conditions de déplacement des voyageurs et au déroulement du voyage depuis sa préparation par le biais d’actions religieuses, et le choix d’un mode de déplacement, ou d’un lieu d’hébergement jusqu’à la manière dont le voyageur se repérait pour trouver son chemin dans un territoire dont il avait une représentation plus ou moins précise. / During the Hellenistic Period there was an intensification of movement within Anatolia. After Alexander’s conquest, Asia Minor became, in effect, for Greeks and Macedonians a necessary stepping stone for travel to the East. This thesis begins by studying the infrastructure which facilitated the mobility of people and goods, in particular roads and their facilities, as well as navigable rivers and maritime routes which connected the harbours of Asia Minor. Different case studies demonstrate the existence of local, regional and supra-regional road-networks in this area. The second part of this thesis explores the influence of institutions on the organisation of circulation and the different ways in which authorities could encourage the circulation of goods and people – includingthe creation of infrastructure, the control of mobility, and different economic and financial policies. In conclusion, this thesis examines the conditions of travel from the perspective of the traveller, including religious preparations, the choice of means of transport and accomodation as well as the means travellers used for planning their journeys and navigating and what these tell us about how space and distance were conceived.

In this paper, two parishes on Gotland have been the focus for intense study regarding the organisation and formation of local communications networks. The parishes of Buttle and Fröjel have been studied to see if it is possible to say anything about local communication during the 6th century, a task that earlier research has shown to be difficult. The parishes represent two different types of landscapes, one costal and one inland. The paper has also asked questions about how the development and quality of the roads and communications networks have changed over time. It also discuss how the topographical- and cultural landscape has influenced the organisation of the communications network. The following questions are asked in this paper; 1. How was local communication (communications between the farmstead, its economic resources and its connections to the larger communications network) in the parishes organised? 2. What can be said about the communications networks development and quality through time? 3. How was the topographical- and cultural landscape organisation connected to the communications network?   The main methodology used in the paper is the retrogressive methodology used to recreate a possible 6th century communications network. This methodology utilises and studies the relationship between the earliest known communications network, registered in the 18th century maps, together with Iron Age sites registered, in the FMIS database, as well as topographical and geological maps to recreate a possible 6th century communications network. The analysis shows that it is hard to grasp the local communications during the 6th century. The local communications only emerge when the local roads merge with the regional ones. In many cases, the local roads were probably not much more than paths in the edges of the fields or only identified by the use of known landmarks. The investigation also shows that the regional (and local) roads were situated closer to the 6th century settlements than previously thought. It is also shown that the development of the road network has steadily lead to a more refined and rationalised network. The largest changes can be related to the 19th century laga skifte and to the later introduction of motor vehicles. Before the 19th century the situation is quite stable, only some minor changes during the 18th century can be seen until you reach the beginning of the middle ages. The major changes probably relate to changes in the landscape organisation in relation to the introduction of Christianity. However, it might also relate to the expansion of cultivated land and the resulting changes of settlement patterns. The investigation also shows that the topographical landscape on Gotland provides little hindrance for the organisation of the landscape. Instead, it feels very much like an artificial landscape where borders and organisation are created by humans, rather than by natural landscape formations. The borders in this case are created by the use of graves and their location in the landscape.

Traditional shortest path algorithms can efficiently find the optimal paths in graphs using simple heuristics. However, formulating a simple heuristic is challenging under the road network setting since there are multiple factors to consider, such as road segment length, edge centrality, and speed limit. This study investigates how a neural network can learn to take these factors as inputs and yield a path given a pair of origin and destination. The research question is formulated as: Are neural networks applicable to real-time route planning tasks in a roadnetwork?. The proposed metric to evaluate the effectiveness of the neural network is arrival rate. The quality of generated paths is evaluated by time efficiency. The real-time performance of the model is also compared between pathfinding in dynamic and static graphs, using theabove metrics. A staggered approach is applied in progressing this investigation. The first step is to generate random graphs, which allows us to monitor the size and properties of the training graph without caring too many details in a road network. The next step is to determine, as a proof of concept, if a neural network can learn to traverse simple graphs with multiple strategies, given that road networks are in effect complex graphs. Finally, we scale up by including factors that might affect the pathfinding in real road networks. Overall, the training data is optimal paths in a graph generated by a shortest path algorithm. The model is then applied to new graphs to generate a path given a pair of origin and destination. The arrival rate and time efficiency are calculated and compared with that of the corresponding optimal path. Experimental results show that the effectiveness, i.e., arrival rate ofthe model is 90% and the path quality, i.e., time efficiency has a medianof 0.88 and a large variance. The experiment shows that the model has better performance in dynamic graphs than in static graphs. Overall, the answer to the research question is positive. However, there is still room to improve the effectiveness of the model and the paths generated by the model. This work shows that a neural network trained to make locally optimal choices can hardly give a globally optimal solution. We also show that our method, only making locally optimal choices, can adapt to dynamic graphs with little performance overhead. / Traditionella algoritmer för att hitta den kortaste vägen kan effektivt hitta de optimala vägarna i grafer med enkel heuristik. Att formulera en enkel heuristik är dock utmanande för vägnätverk eftersom det finns flera faktorer att överväga, såsom vägsegmentlängd, kantcentralitet och hastighetsbegränsningar. Denna studie undersöker hur ett neuralt nätverk kan lära sig att ta dessa faktorer som indata och finna en väg utifrån start- och slutpunkt. Forskningsfrågan är formulerad som: Är neuronnätverket tillämpliga på realtidsplaneringsuppgifter i ett vägnät?. Det föreslagna måttet för att utvärdera effektiviteten hos det neuronnätverket är ankomstgrad. Kvaliteten på genererade vägar utvärderas av tidseffektivitet. Prestandan hos modellen jämförs också mellan sökningen i dynamiska och statiska grafer, med hjälp av ovanstående mätvärden. Undersökningen bedrivs i flera steg. Det första steget är att generera slumpmässiga grafer, vilket gör det möjligt för oss att övervaka träningsdiagrammets storlek och egenskaper utan att ta hand om för många detaljer i ett vägnät. Nästa steg är att, som ett bevis på konceptet, undersöka om ett neuronnätverk kan lära sig att korsa enkla grafer med flera strategier, eftersom vägnätverk är i praktiken komplexa grafer. Slutligen skalas studien upp genom att inkludera faktorer som kan påverka sökningen i riktiga vägnät. Träningsdata utgörs av optimala vägar i en graf som genereras av en algoritm för att finna den kortaste vägen. Modellen appliceras sedan i nya grafer för att hitta en väg mellan start och slutpunkt. Ankomstgrad och tidseffektivitet beräknas och jämförs med den motsvarande optimala sökvägen. De experimentella resultaten visar att effektiviteten, dvs ankomstgraden av modellen är 90% och vägkvaliteten dvs tidseffektiviteten har en median på 0,88 och en stor varians. Experimentet visar att modellen har bättre prestanda i dynamiska grafer än i statiska grafer. Sammantaget är svaret på forskningsfrågan positivt. Det finns dock fortfarande utrymme att förbättra modellens effektivitet och de vägar som genereras av modellen. Detta arbete visar att ett neuronnätverk tränat för att göra lokalt optimala val knappast kan ge globalt optimal lösning. Vi visar också att vår metod, som bara gör lokalt optimala val, kan anpassa sig till dynamiska grafer med begränsad prestandaförlust.

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Le Ministère des Ressources Naturelles et de la Faune (MRNF) a mandaté la compagnie de géomatique SYNETIX inc. de Montréal et le laboratoire de télédétection de l’Université de Montréal dans le but de développer une application dédiée à la détection automatique et la mise à jour du réseau routier des cartes topographiques à l’échelle 1 : 20 000 à partir de l’imagerie optique à haute résolution spatiale. À cette fin, les mandataires ont entrepris l’adaptation du progiciel SIGMA0 qu’ils avaient conjointement développé pour la mise à jour cartographique à partir d’images satellitales de résolution d’environ 5 mètres. Le produit dérivé de SIGMA0 fut un module nommé SIGMA-ROUTES dont le principe de détection des routes repose sur le balayage d’un filtre le long des vecteurs routiers de la cartographie existante. Les réponses du filtre sur des images couleurs à très haute résolution d’une grande complexité radiométrique (photographies aériennes) conduisent à l’assignation d’étiquettes selon l’état intact, suspect, disparu ou nouveau aux segments routiers repérés. L’objectif général de ce projet est d’évaluer la justesse de l’assignation des statuts ou états en quantifiant le rendement sur la base des distances totales détectées en conformité avec la référence ainsi qu’en procédant à une analyse spatiale des incohérences. La séquence des essais cible d’abord l’effet de la résolution sur le taux de conformité et dans un second temps, les gains escomptés par une succession de traitements de rehaussement destinée à rendre ces images plus propices à l’extraction du réseau routier. La démarche globale implique d’abord la caractérisation d’un site d’essai dans la région de Sherbrooke comportant 40 km de routes de diverses catégories allant du sentier boisé au large collecteur sur une superficie de 2,8 km2. Une carte de vérité terrain des voies de communication nous a permis d’établir des données de référence issues d’une détection visuelle à laquelle sont confrontés les résultats de détection de SIGMA-ROUTES. Nos résultats confirment que la complexité radiométrique des images à haute résolution en milieu urbain bénéficie des prétraitements telles que la segmentation et la compensation d’histogramme uniformisant les surfaces routières. On constate aussi que les performances présentent une hypersensibilité aux variations de résolution alors que le passage entre nos trois résolutions (84, 168 et 210 cm) altère le taux de détection de pratiquement 15% sur les distances totales en concordance avec la référence et segmente spatialement de longs vecteurs intacts en plusieurs portions alternant entre les statuts intact, suspect et disparu. La détection des routes existantes en conformité avec la référence a atteint 78% avec notre plus efficace combinaison de résolution et de prétraitements d’images. Des problèmes chroniques de détection ont été repérés dont la présence de plusieurs segments sans assignation et ignorés du processus. Il y a aussi une surestimation de fausses détections assignées suspectes alors qu’elles devraient être identifiées intactes. Nous estimons, sur la base des mesures linéaires et des analyses spatiales des détections que l’assignation du statut intact devrait atteindre 90% de conformité avec la référence après divers ajustements à l’algorithme. La détection des nouvelles routes fut un échec sans égard à la résolution ou au rehaussement d’image. La recherche des nouveaux segments qui s’appuie sur le repérage de points potentiels de début de nouvelles routes en connexion avec les routes existantes génère un emballement de fausses détections navigant entre les entités non-routières. En lien avec ces incohérences, nous avons isolé de nombreuses fausses détections de nouvelles routes générées parallèlement aux routes préalablement assignées intactes. Finalement, nous suggérons une procédure mettant à profit certaines images rehaussées tout en intégrant l’intervention humaine à quelques phases charnières du processus. / In order to optimize and reduce the cost of road map updating, the Ministry of Natural Resources and Wildlife is considering exploiting high definition color aerial photography within a global automatic detection process. In that regard, Montreal based SYNETIX Inc, teamed with the University of Montreal Remote Sensing Laboratory (UMRSL) in the development of an application indented for the automatic detection of road networks on complex radiometric high definition imagery. This application named SIGMA-ROUTES is a derived module of a software called SIGMA0 earlier developed by the UMRSL for optic and radar imagery of 5 to 10 meter resolution. SIGMA-ROUTES road detections relies on a map guided filtering process that enables the filter to be driven along previously known road vectors and tagged them as intact, suspect or lost depending on the filtering responses. As for the new segments updating, the process first implies a detection of potential starting points for new roads within the filtering corridor of previously known road to which they should be connected. In that respect, it is a very challenging task to emulate the human visual filtering process and further distinguish potential starting points of new roads on complex radiometric high definition imagery. In this research, we intend to evaluate the application’s efficiency in terms of total linear distances of detected roads as well as the spatial location of inconsistencies on a 2.8 km2 test site containing 40 km of various road categories in a semi-urban environment. As specific objectives, we first intend to establish the impact of different resolutions of the input imagery and secondly establish the potential gains of enhanced images (segmented and others) in a preemptive approach of better matching the image property with the detection parameters. These results have been compared to a ground truth reference obtained by a conventional visual detection process on the bases of total linear distances and spatial location of detection. The best results with the most efficient combination of resolution and pre-processing have shown a 78% intact detection in accordance to the ground truth reference when applied to a segmented resample image. The impact of image resolution is clearly noted as a change from 84 cm to 210 cm resolution altered the total detected distances of intact roads of around 15%. We also found many roads segments ignored by the process and without detection status although they were directly liked to intact neighbours. By revising the algorithm and optimizing the image pre-processing, we estimate a 90% intact detection performance can be reached. The new segment detection is non conclusive as it generates an uncontrolled networks of false detections throughout other entities in the images. Related to these false detections of new roads, we were able to identify numerous cases of new road detections parallel to previously assigned intact road segments. We conclude with a proposed procedure that involves enhanced images as input combined with human interventions at critical level in order to optimize the final product.

Le Ministère des Ressources Naturelles et de la Faune (MRNF) a mandaté la compagnie de géomatique SYNETIX inc. de Montréal et le laboratoire de télédétection de l’Université de Montréal dans le but de développer une application dédiée à la détection automatique et la mise à jour du réseau routier des cartes topographiques à l’échelle 1 : 20 000 à partir de l’imagerie optique à haute résolution spatiale. À cette fin, les mandataires ont entrepris l’adaptation du progiciel SIGMA0 qu’ils avaient conjointement développé pour la mise à jour cartographique à partir d’images satellitales de résolution d’environ 5 mètres. Le produit dérivé de SIGMA0 fut un module nommé SIGMA-ROUTES dont le principe de détection des routes repose sur le balayage d’un filtre le long des vecteurs routiers de la cartographie existante. Les réponses du filtre sur des images couleurs à très haute résolution d’une grande complexité radiométrique (photographies aériennes) conduisent à l’assignation d’étiquettes selon l’état intact, suspect, disparu ou nouveau aux segments routiers repérés. L’objectif général de ce projet est d’évaluer la justesse de l’assignation des statuts ou états en quantifiant le rendement sur la base des distances totales détectées en conformité avec la référence ainsi qu’en procédant à une analyse spatiale des incohérences. La séquence des essais cible d’abord l’effet de la résolution sur le taux de conformité et dans un second temps, les gains escomptés par une succession de traitements de rehaussement destinée à rendre ces images plus propices à l’extraction du réseau routier. La démarche globale implique d’abord la caractérisation d’un site d’essai dans la région de Sherbrooke comportant 40 km de routes de diverses catégories allant du sentier boisé au large collecteur sur une superficie de 2,8 km2. Une carte de vérité terrain des voies de communication nous a permis d’établir des données de référence issues d’une détection visuelle à laquelle sont confrontés les résultats de détection de SIGMA-ROUTES. Nos résultats confirment que la complexité radiométrique des images à haute résolution en milieu urbain bénéficie des prétraitements telles que la segmentation et la compensation d’histogramme uniformisant les surfaces routières. On constate aussi que les performances présentent une hypersensibilité aux variations de résolution alors que le passage entre nos trois résolutions (84, 168 et 210 cm) altère le taux de détection de pratiquement 15% sur les distances totales en concordance avec la référence et segmente spatialement de longs vecteurs intacts en plusieurs portions alternant entre les statuts intact, suspect et disparu. La détection des routes existantes en conformité avec la référence a atteint 78% avec notre plus efficace combinaison de résolution et de prétraitements d’images. Des problèmes chroniques de détection ont été repérés dont la présence de plusieurs segments sans assignation et ignorés du processus. Il y a aussi une surestimation de fausses détections assignées suspectes alors qu’elles devraient être identifiées intactes. Nous estimons, sur la base des mesures linéaires et des analyses spatiales des détections que l’assignation du statut intact devrait atteindre 90% de conformité avec la référence après divers ajustements à l’algorithme. La détection des nouvelles routes fut un échec sans égard à la résolution ou au rehaussement d’image. La recherche des nouveaux segments qui s’appuie sur le repérage de points potentiels de début de nouvelles routes en connexion avec les routes existantes génère un emballement de fausses détections navigant entre les entités non-routières. En lien avec ces incohérences, nous avons isolé de nombreuses fausses détections de nouvelles routes générées parallèlement aux routes préalablement assignées intactes. Finalement, nous suggérons une procédure mettant à profit certaines images rehaussées tout en intégrant l’intervention humaine à quelques phases charnières du processus. / In order to optimize and reduce the cost of road map updating, the Ministry of Natural Resources and Wildlife is considering exploiting high definition color aerial photography within a global automatic detection process. In that regard, Montreal based SYNETIX Inc, teamed with the University of Montreal Remote Sensing Laboratory (UMRSL) in the development of an application indented for the automatic detection of road networks on complex radiometric high definition imagery. This application named SIGMA-ROUTES is a derived module of a software called SIGMA0 earlier developed by the UMRSL for optic and radar imagery of 5 to 10 meter resolution. SIGMA-ROUTES road detections relies on a map guided filtering process that enables the filter to be driven along previously known road vectors and tagged them as intact, suspect or lost depending on the filtering responses. As for the new segments updating, the process first implies a detection of potential starting points for new roads within the filtering corridor of previously known road to which they should be connected. In that respect, it is a very challenging task to emulate the human visual filtering process and further distinguish potential starting points of new roads on complex radiometric high definition imagery. In this research, we intend to evaluate the application’s efficiency in terms of total linear distances of detected roads as well as the spatial location of inconsistencies on a 2.8 km2 test site containing 40 km of various road categories in a semi-urban environment. As specific objectives, we first intend to establish the impact of different resolutions of the input imagery and secondly establish the potential gains of enhanced images (segmented and others) in a preemptive approach of better matching the image property with the detection parameters. These results have been compared to a ground truth reference obtained by a conventional visual detection process on the bases of total linear distances and spatial location of detection. The best results with the most efficient combination of resolution and pre-processing have shown a 78% intact detection in accordance to the ground truth reference when applied to a segmented resample image. The impact of image resolution is clearly noted as a change from 84 cm to 210 cm resolution altered the total detected distances of intact roads of around 15%. We also found many roads segments ignored by the process and without detection status although they were directly liked to intact neighbours. By revising the algorithm and optimizing the image pre-processing, we estimate a 90% intact detection performance can be reached. The new segment detection is non conclusive as it generates an uncontrolled networks of false detections throughout other entities in the images. Related to these false detections of new roads, we were able to identify numerous cases of new road detections parallel to previously assigned intact road segments. We conclude with a proposed procedure that involves enhanced images as input combined with human interventions at critical level in order to optimize the final product.

Phytophthora lateralis is the causal agent of cedar root rot, a fatal forest pathogen whose principal host is Chamaecyparis lawsoniana (Port-Orford-cedar), a predominantly riparian-restricted endemic tree species of ecological, economical, and cultural importance to coastal Oregon and California. Local scale distribution of P. lateralis is thought to be associated with timber harvest and road-building disturbances. However, knowledge of the landscape-scale factors that contribute to successful invasions of P. lateralis is also important for effective land management of Port-Orford-cedar. P. lateralis is able to infest in wet conditions via stream networks (zoospore) and dry conditions via road networks (resting spore). This study tested the hypothesis that vehicles spread P. lateralis by relating its distribution to traffic intensive, anthropogenic disturbances (i.e. a road network, timber harvest) over a 31-yr period in a 3,910-km² portion of the Rogue River-Siskiyou National Forest in the Siskiyou Mountains of Oregon. Indices of road disturbance (presence/absence, configuration, length, density, road-stream network connectivity) and timber harvest (presence/absence, area, density, frequency) were related to locations of infested cedar populations from a USFS survey dataset using a geographic information system (GIS). About 40% of 934 7th-field catchments were infested with the pathogen. Total road length of the study site was 5,070 km; maximum road density was 8.2 km/km2 and averaged 1.6 km/km² in roaded catchments (n = 766). Timber activities extracted 17,370 ha (2,338 cutting units) of forest across 509 catchments; 345 catchments were cut ≥ twice. Maximum harvest density was 0.92 km²/km² ([mean] = 0.04). Both road networks and timber harvest patchworks were significantly related to cedar root rot heterogeneity. Chi-squared contingency tables showed that infestation rates were 2.2 times higher in catchments with roads compared to roadless catchments and 1.4 times higher in catchments with road-stream intersections compared to those that were unconnected. Infestation was twice as likely in catchments with both harvest and road presence than road presence alone. Single-variable logistic regression showed that a one percent increase in harvest density increased infestation odds 25% and a one-unit (km/km²) increase in road density increased infestation odds 80%. Road and stream network configuration was also important to pathogen distribution: 1) uninfested catchments are most likely to be spatially removed from infested, roaded catchments, 2) only 11% of 287 roaded catchments downstream of infested, roaded catchments were uninfested, and 3) only 12% of 319 catchments downstream of infested catchments were uninfested. Road networks and timber harvest patchworks appear to reduce landscape heterogeneity by providing up-catchment and down-catchment access to host populations by linking pathogenic materials to the stream network. Timber harvest data suggest that while infestation risk to Port-Orford-cedar populations remains high, management policies may have curbed infestation risk in timber-harvested catchments; if this is a result of specific P. lateralis mitigation policies adopted in the late 1980's or broader, region-wide conservation policies (i.e. the Northwest Forest Plan) is yet unclear. / Graduation date: 2012