土地総合研究 2024年冬号

45

特 集  土 地 ・ 空 間 DX Urban Landscape Mapping: Estimating Building
Coordinates Using Smartphone Camera andGeospatial AI TechniquesUrbanX Technologies, Inc.

Abstract

  

  

Accurate localization of buildings is an important task in urban planning and development,
impacting areas such as emergency response, navigation, and augmented reality. This researchpresents several advancements in urban building mapping using moving smartphone camera
images, crucial for maintaining precise geographic information systems. We created acomprehensive dataset, comprising more than 7,000 instances of diverse building types, including
low-rise residence, apartment, high-rise residence for the detection and classification of urban
buildings. Utilizing YOLOv8, a highly accurate building detection model was developed,
achieving an mAP of 76%. This work introduces an innovative integration of AI algorithms withgeospatial data and photogrammetry techniques to accurately localize buildings from smartphone
camera images. The precision of our building localization method is evaluated, with an error
margin of up to 5.22 meters. Moreover, by identifying and analyzing the sources of errors in thelocalization process, this study provides insights into potential areas for improvement, setting thestage for future enhancements in urban mapping technologies. As well as providing support for
urban planning and emergency services, we believe that this research has implications fordelivery services, disaster management, and preservation efforts, indicating a significant impact
on both societal and technological domains.
Keywords: building detection, building id, urban monitoring, YOLOv8

1 Introduction

utilizedbyAccurately building localization is an important

individuals.

governments,

businesses

andaspect of urban planning and development. It

The building locations data not only supports

holds importance in societal and technologicalurban planning but also aids emergency services

domains, including emergency response, urban

in responding promptly. Additionally it plays aplanning, navigation systems and augmentedrole, in the development of city initiatives.

reality applications. Given the growth of areas,

Furthermore precise building maps can improve

efficiently mapping and updating building

delivery services, assist in disaster management

coordinates is crucial, for maintaining precise

efforts and contribute to preservation endeavors.

geographic information systems (GIS) that are

Recent studies have primarily focused on

46

土地総合研究 2024年冬号

using static surveillance cameras or aerial

and photogrammetry to localize buildings

imagery for urban mapping. However, theseaccurately. This methodology represents anmethods are often constrained by the limited

advancement in the precision of urban

field of view and the inability to capture real-time

mapping technologies.

changes in urban landscapes. For instance,• Evaluation of Localization Accuracy: Theaerial imagery based studies, like the one

accuracy of our building localization method is

conducted in [1], provided broad overviews but

rigorously evaluated, showing an error margin

were limited in terms of real-time dataof up to 5.22 meters, which is crucial for

acquisition.

understanding the reliability and potential

To overcome these limitations, our research

applications of our approach.

presents a novel approach that utilizes moving• Error Sources in Localization: By analyzing

smartphone cameras, leveraging the ubiquity

the sources of errors in our localization process,and mobility of these devices. This method aligns

we provide insights into potential areas forwith the recent trend of using moving cameras

improvement and pave the way for futurefor dynamic data acquisition as seen in studies

enhancements in urban mapping techniques.

such as [2], where vehicle-mounted cameras

Our methodology not only allows for real-timewere used for traffic flow analysis.

urban mapping but also addresses some of theThis research makes several key contributions tolimitations faced by previous research. Unlike

the field of urban building mapping:static camera methods, our approach captures• A Large-Scale Building Detection Dataset: We

the dynamic changes in urban environments.

developed a comprehensive dataset consistingMoreover, by analyzing and identifying theof more than 7,000 instances of buildings insources of errors in building localization, thisTokyo metropolitan area, focused on threestudy provides insights for future enhancements

distinct types of buildings: low-rise residence,in urban mapping technologies.

apartment, and high-rise residence.
• Building

Detection

Model:

Utilizingthe

2 Methodology

advanced capabilities of YOLOv8, we trained a

We show the overall framework for building

model that achieves a 76% mAP in building

localization in Figure 1, which has components,

detection.

such as building detection, building distance• Integration

of

AI

with

Geospatial

andestimation, building

angle

estimation

andPhotogrammetry Techniques: Our approachbuilding localization. In the next few sub-sections,combines AI algorithms with geospatial data

we describe each component in detail.

Fig. 1: Illustration of building detection and localization using a moving smartphone camera.

土地総合研究 2024年冬号

47

resources required for training and computation.

2.1 Building Detection Dataset
For the localization of individual buildings, it is

We carry out a driving experiment in Tokyo

necessary to detect them first. There are several

metropolitan area and record videos using

ways of building detection, such as using object

smartphone mounted on the dashboard of a car.

detection

method,We manually annotate buildings in the extracted

semantic

segmentation

instance

segmentation,andpanoptic

frames from the video by drawing rectangularsegmentation, etc. Out of all these methods, webounding boxes and categorize them into threeselected, object detection method to detect

categories based on their height, such as low-risebuildings as rectangular bounding boxes. We

buildings (up to four floors), mid-rise building

select this method due to the simplicity of the(four to fifteen floors) and high-rise buildings

development of the dataset annotation and(above fifteen floors), as shown in Table 1.


Table 1: Building categories for annotation and approximate height characteristics
Type of Building

Number of Stories (approx.)

Low-rise residence

Up to four

Apartment

Five to Fifteen

High-rise residence

Example image

Above twelve

(a) Example annotation of three classes of buildings(b) Distribution of building classes in the training set

Fig. 2: Building annotation example and distribution in the training dataset

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土地総合研究 2024年冬号

In total, we annotate 7,174 buildings forutilizes a split Ultralytics head that is anchorfree,

training and 304 buildings for validation. An

enhancing detection accuracy and efficiency. It

example annotation along with the distribution

optimizes the accuracy-speed tradeoff, ensuringof the three types of buildings are shown insuitability for real-time object detection in

Figure 2. We use this dataset for training

diverse areas. YOLOv8 also offers a variety ofbuilding detection model.

pre-trained models, catering to different tasks
and performance requirements.



YOLOv8l, one of the larger models in the

2.2 Training Building Detection Model
We use the building detection dataset consisting

YOLOv8 series, is particularly noteworthy for itsof 7,147 buildings to train an object detection

performance metrics. It achieves a balance

network YOLOv8 [3]. YOLOv8 is the latest

between speed and accuracy, standing out as aaddition to the YOLO (You Only Look Once)

preferred choice for real-time detection tasks. We

series of real-time object detectors, offeringuse YOLOv8l model using the initial pre-trained

remarkable performance. For improved feature

weights on the COCO dataset [4] to train theextraction and object detection performance,building detection model for 100 epochs and

YOLOv8 features advanced backbone and neckselect the best weights on the validation set forarchitectures.

further calculations. The hyperparameters used

In contrast to anchor-based approaches, YOLOv8

during training are shown in Table 2.


Table 2: Top 5 Hyperparameters for Training YOLOv8

Hyperparameter

Value

Initial Learning Rate (lr0)0.01

Weight Decay

0.0005

Momentum

0.937

Nominal Batch Size (nbs)

64

Mosaic Augmentation (mosaic)

1.0

DetailSets the starting learning rate, crucial for the early stages of the
training process.
Helps prevent overfitting by penalizing large weights, importantfor model regularization.
Accelerates SGD in the right direction, critical for faster andmore stable convergence.
Influences the speed and stability of the training process, as wellas the generalization ability of the model.
Data augmentation technique, enhances the model’s ability togeneralize and recognize objects in various scenarios.

土地総合研究 2024年冬号

49

2.3 Building Localizationeach with a predefined average height, as

Building localization refers to the estimation of

explained before.

latitude and longitude of the detected buildings

In digital cameras, such as smartphones,

from the smartphone camera images. To

object dimensions are typically measured in

calculate the GPS coordinates of identified

pixels. This pixel measurement can be convertedbuildings from the known GPS location of the ego
vehicle, two key parameters are essential: thedistance 𝒟𝒟 to the detected buildings from theego vehicle, and the bearing 𝛽𝛽𝑟𝑟𝑟𝑟𝑟𝑟 . This bearing

𝛽𝛽𝑟𝑟𝑟𝑟𝑟𝑟 denotes the direction in which we musttravel the distance 𝒟𝒟 from the ego vehicle toarrive at the GPS location of the buildings. 𝛽𝛽𝑟𝑟𝑟𝑟𝑟𝑟

to metric units using the formula 𝐻𝐻𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 =

𝐼𝐼𝐻𝐻

,

where 𝜇𝜇 is the sensor height in millimeters, 𝐻𝐻𝑝𝑝𝑝𝑝

is the object’s height in pixels, and 𝐼𝐼𝐻𝐻 is theimage height in pixels. By substituting thesevalues into Equation 1 and rearranging, we
obtain the distance to the building in meters:

is obtained by considering the bearing of ego

vehicle 𝛽𝛽𝑓𝑓𝑓𝑓𝑓𝑓 and the angle the detected vehicles

μ×𝐻𝐻𝑝𝑝𝑝𝑝

𝒟𝒟 =𝑓𝑓 × 𝐻𝐻𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 × 𝐼𝐼𝐻𝐻  (2)
𝐻𝐻𝑝𝑝𝑝𝑝 × μ

make with respect to the smartphone camera or

We consider different Hactual for different

ego vehicle. In the next three sub-sections, wecategories of buildings, as shown in Table 3. It

explain the estimation of building, angle

should be noted that heights of building is not

estimation and localization of building using thealways fixed and because of this there might be

GPS position of the ego vehicle.

some error in the calculated distance 𝒟𝒟 .

However, the estimated distance does not have to
2.3.1 Building Distance Estimation

be precise as building dimensions are also large,

To calculate the distance to detected buildings,

which

gives

extra

tolerancefor

position

we employ a photogrammetry method proposed

estimation considering the error in the distancein research conducted by Kumar et al. [2, 5]. Thisdue to fixed height of the buildings.

technique is based on the principle that the ratioof an object’s actual size to its size in an image isTable 3: Reference building heights 𝐻𝐻𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎

equal to the ratio of the object’s distance from thecamera sensor to the camera’s focal length. This
relationship is mathematically expressed as:
𝐻𝐻𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝐷𝐷=   (1)
𝐻𝐻𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑓𝑓

Here, 𝐻𝐻𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 represents the real height ofthe building, 𝐻𝐻𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 is the building’s height in
the image, 𝐷𝐷 denotes the distance to thebuilding, and 𝑓𝑓 is the focal length of the camera.

We focus on measuring the height of buildings

instead of their width due to the consistentnature of height in varying perspectives anddistances in images. Buildings are categorized
into types such as low rise, mid rise, and high rise,

Building Class

Low-rise (low-rise residence)

Building Height 𝑯𝑯𝒂𝒂𝒂𝒂𝒂𝒂𝒂𝒂𝒂𝒂𝒂𝒂

Mid-rise (apartment)

50 meters

High-rise (high-rise residence)

80 meters

30 meters
2.3.2 Building Angle Estimation
Angle estimation involves calculating the angle
between the direction of the vehicle (ego vehicle)
and the detected building, using principles ofphotogrammetry, camera calibration data, andgeometric calculations. We calibrate the camera
in advance using a chessboard pattern. The

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土地総合研究 2024年冬号

camera calibration coefficients include the2.3.3 Building Position Estimation

cameracoefficients,

The relative bearing to the building is calculated

denoted as 𝐾𝐾. These coefficients are critical forby adjusting the detected angle 𝜃𝜃𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 withmatrixanddistortion

correcting lens distortion and transforming 2D
image points into 3D world points using theinverse of the camera matrix, 𝐾𝐾−1.

For each building detected in the frame, its

centralpixel

coordinates(𝑐𝑐𝑥𝑥 , 𝑐𝑐𝑦𝑦 )aredetermined as the center of the bounding boxes.
Using the inverse camera matrix 𝐾𝐾

−1

, we

transform these coordinates to a normalized
camera coordinate system:
𝑐𝑐𝑥𝑥
𝑅𝑅𝐼𝐼 = 𝐾𝐾 −1 ⋅ [𝑐𝑐𝑦𝑦 ]   (3)1This produces a ray RI originating from thethe ego vehicle’s forward bearing 𝛽𝛽𝑓𝑓𝑓𝑓𝑓𝑓 :𝛽𝛽𝑟𝑟𝑟𝑟𝑟𝑟 = (𝛽𝛽𝑓𝑓𝑓𝑓𝑓𝑓 − 𝜃𝜃𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 ) mod 360  (7)This relative bearing 𝛽𝛽𝑟𝑟𝑟𝑟𝑟𝑟 is used along withthe known distance 𝐷𝐷 to compute the GPS
position of the building.
The

GPS

positionofthebuilding(φbldg𝑖𝑖 , λbldg𝑖𝑖 ) is calculated using the geodesic
method [6] by considering the current GPS

position of the ego vehicle and moving a distance
𝒟𝒟 at bearing 𝛽𝛽𝑟𝑟𝑟𝑟𝑟𝑟 .


2.4 Localization Error Evaluation

camera center and intersecting the detected

This section outlines the methodology employed,

for estimating the error in the estimated buildingbuilding.

The

reference

direction

𝑅𝑅𝐶𝐶

corresponding to the camera’s optical axis, isdetermined using 𝐾𝐾 −1 and the image center. The

cosine of the angle between 𝑅𝑅𝐼𝐼 and 𝑅𝑅𝐶𝐶 iscalculated using their dot product:cos(θ) =𝑅𝑅𝐼𝐼 ⋅ 𝑅𝑅𝐶𝐶
  (4)‖𝑅𝑅𝐼𝐼 ‖‖𝑅𝑅𝐶𝐶 ‖To ascertain the precise direction towards thedetected building, the angle is determined using
the cross product of vectors 𝑅𝑅𝐶𝐶 and 𝑅𝑅𝐼𝐼 . The crossproduct helps in identifying the orientation of 𝑅𝑅𝐼𝐼

relative to 𝑅𝑅𝐶𝐶 . The Z-component of the crossproduct, denoted as ⃗⃗⃗𝑣𝑣𝑧𝑧 , indicates whether 𝑅𝑅𝐼𝐼

lies to the left or right of 𝑅𝑅𝐶𝐶 . This is essential for

determining the correct sign of the angle. The
equation for the cross product is given by:
𝑣𝑣 = 𝑅𝑅𝐶𝐶 × 𝑅𝑅𝐼𝐼   (5)Where 𝑣𝑣 represents the vector resulting

from the cross product. Based on the sign of theZcomponent of 𝑣𝑣 , the angle 𝜃𝜃 is adjusted
accordingly:−𝜃𝜃 𝑖𝑖𝑖𝑖 ⃗⃗⃗𝑣𝑣𝑧𝑧 < 0  (6)𝜃𝜃𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 = {𝜃𝜃 𝑜𝑜𝑜𝑜ℎ𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒

position (φbldg𝑖𝑖 , λbldg𝑖𝑖 ). The error is estimated asfollows:ͳǤ For each estimated position (φbldg𝑖𝑖 , λbldg𝑖𝑖 ), its
location is first verified on a map.

ʹǤ If the estimated position falls directly on thebuilding, the error is considered to be zero
meters, indicating a precise estimation.

͵Ǥ If the estimated position is outside thebuilding’s periphery, the nearest point on thebuilding’s outline is identified.

ͶǤ The error is then calculated as the geodesic
distance [7] between the estimated position(φbldg𝑖𝑖 , λbldg𝑖𝑖 ) and the nearest point on thebuilding(φbldg_boundary𝑖𝑖 , λbldg_boundary𝑖𝑖 ) .

Thisdistance is computed on the Earth’s ellipsoidal
surface,which

offersamoreaccurate

representation than a simple spherical model.
Mathematically, the error for the 𝑖𝑖 𝑡𝑡ℎ building 𝔼𝔼𝑖𝑖
is given by:
𝔼𝔼𝑖𝑖= 𝒈𝒈𝒈𝒈𝒈𝒈𝒈𝒈𝒈𝒈𝒈𝒈𝒈𝒈𝒈𝒈_𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅 ((φbldg𝑖𝑖 , λbldg𝑖𝑖 ), (φbldg_boundary𝑖𝑖 , λbldg_boundary𝑖𝑖 ))̅ ) over all 𝑛𝑛 samples is
The mean error (𝔼𝔼calculated as:

土地総合研究 2024年冬号

̅=𝔼𝔼

3 Results𝑛𝑛

1∑ 𝔼𝔼𝑖𝑖   (8)
𝑛𝑛
𝑖𝑖=1



51

different examples in Table 4, we notice that we
can estimate location of buildings accurately forvarious types of buildings, such as mansion,
tower mansions, etc.
From the results in Table 4, we can see that

3.1 Building Detection
We evaluate the accuracy of the YOLOv8 modelin every frame, several buildings can be detected.

trained on the validation set of the building

However, we only consider the nearest buildings

detection dataset. We present the results of(shownprecision, recall and mAP in Figure 3. From

localization within a threshold distance of 50

Figure 3, we notice that all three metrics

meters.

using

green

boundingboxes)

forprecision, recall and mAP increases as theWe evaluate the error (using Equation 8) innumber of epochs increases and reaches alocalization using 60 samples of buildings from aplateau after 60 epochs. We obtain the highest

test drive in the Tokyo metropolitan area andmAP of 0.762 at 85 epoch and use this weight for

present result in Figure 4. From the histogram

further detection of buildings.

presented in Figure 4a, we notice that in several
instances the error is 0 meters because the3.2 Building Localization

localized position lies on/inside the periphery of

In Table 4, we show the localization of detected

the detected building. We obtain an overall error

building on the map along with car’s GPSof 5.22 meters.

location (shown using blue marker). From

Fig. 3: Progression of mAP 50, Precision, and Recall over different epochs with highlighted maximummAP 50.

52

土地総合研究 2024年冬号

Table 4: Car’s GPS position, detected building location and visualization on map. Image courtesy of
USGS Earth Explorer. © 2024/01/21.
Car’s GPS

Building Detection

Target Building Localized GPS on map

35.660681378870244,
35.660298, 139.8037941

139.80357100495178

35.66229062062282,
35.661935, 139.8036792

139.8039596017458

35.6625822,

35.662903031199804,

139.8048738

139.80515298698734

35.6564844,

35.65726367288982,

139.7991766

139.79965628513142

土地総合研究 2024年冬号

53ȋȌ Histogram of error values in building localization with mean errorȋȌ Localized position visualization on map. Green bars show the ground truth while blue barsrepresent
predicted position. Map data: © OpenStreetMap contributors, CC-BY-SA

Fig. 4: Histogram of error values in building localization along with estimated and ground truth
building GPS position visualized on map for a sample driving experiment

54

土地総合研究 2024年冬号

In Figure 4b, we also present visualization ofsuch as the localized position being either

predicted and ground truth positions of buildings.

overpasses the detected building or lies in front

To avoid crowding of points for closer buildings orof the building. Such phenomenon occur mainly

when the same building is localized fromdue to two reasons. The first reason is the fixed

different angles, we consider a distance thresholdheight consideration 𝐻𝐻𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 in Equation 2. Suchto only consider a small sample.


problems could be fixed using depth estimation

techniques that does not require consideration of

4 Discussions

object dependent parameters, such as using

In the results for building detection, we observed

Depththat the mean Average Precision (mAP) value forcomputational cost. The second reason is also duebuilding detection is 0.762. This indicates thatto error inherent in the ego vehicle GPS location.

we can detect buildings with high accuracy. It

Theshould be noted that building types can vary inpositioning error of a few meters, especially inshape and size, even within the same category,the dense areas due to factors, such as multipath

such as midrise buildings (mansions), which

error, signal blockage, etc. [9]. The initial error inleads to some errors in the classification. In

the source may lead to error propagation in the

addition to variations within the same category,

estimated distance.

Anything[8]

smartphone’GPS

at

the

sensorexpense

may

of

have

the appearance of buildings also changes as the

Building position estimation requires theego vehicle approaches them. For example, aforward bearing of the ego vehicle, which is

tower mansion is detected and categorizedcalculated using GPS position at two timestamps

correctly when viewed from a distance. However,𝑡𝑡 and 𝑡𝑡 + 1. When the vehicle is moving in thewhen the ego vehicle is close to the building, thefull view is not visible, and it may be

forward direction, the GPS positions at two
timestamps lie in the direction of the vehicle

misclassified as a mansion. Misclassification of

movement. The problem arises when the vehiclebuildings does not significantly affect theis at rest. In such cases, the recorded positions

accuracy of localization since this categorization

may not be in the direction of the movement andis mainly used for distance estimation, as

may lie laterally to the car direction. This causes

indicated by the real height of the building

the change in the direction of bearing leading to(𝐻𝐻𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 ) in Equation 2. The reference heights ofincorrect localization, as illustrated in Figure 5.

mansions and tower mansions do not vary

In such cases, it is necessary to consider either

significantly, as shown in Table 3.

moving average of the past GPS traces or

From the building localization results, we findthat buildings can be localized accurately. In
some cases, we notice some error in localization,

consider the bearing of the vehicle only when thevehicle is moving.
土地総合研究 2024年冬号

55

Fig. 5: Bearing direction estimation when the car is moving (a) and when it is at rest (b) and (c). Whenthe vehicle is at rest, the GPS position of the smartphone at two different times may lie lateral(sideways) to the direction of the vehicle, as shown in b) and c).


5 Conclusions

will be crucial for enhancing the accuracy ofIn this research, we introduce a framework for

urban mapping technologies.

the accurate localization of urban buildings using

Our

proposedmethodexemplifies

themoving smartphone cameras. We develop apotential of integrating AI algorithms withbuilding detection dataset containing more thanphotogrammetry and geospatial data, especially

7,000 instances of various types of buildings. A

when leveraging the mobility and ubiquity of

key component of our methodology was thesmartphones. This technique not only enables

training

which

dynamic mapping of urban landscapes but also

demonstrated high accuracy in detecting aaddresses the limitations of static observational

diverse range of building types, such as low-risemethods.

ofthe

YOLOv8model,

residence, apartment, etc. with an mAP of 76%.

The implications of our work extend beyond

We integrate the building detection results withthe realm of urban planning and development.

distance and angle estimation and estimate

Thebuilding location by considering ego vehicle GPS

significantly benefit emergency response systems,

position with distance and bearing of thenavigation applications, and augmented reality

detected vehicles. Using our localization method,experiences.

Additionally,

we achieve accuracy of 5.22 meters. In addition,

applications

inour research examines the causes of localization

management, and preservation efforts highlighterrors, providing valuable insights for future

the societal impact of this research. While theimprovements. We identified challenges such as

current

the fixed height consideration in distanceadvancements in AI and geospatial technologies

estimation and the inherent inaccuracies inare expected to further refine and expand thesmartphone GPS data. Addressing these issues

capabilities of urban mapping methods in the

preciselocalization

results

deliveryareofbuildings

can

the

potential

services,disasterpromising,ongoing

56

土地総合研究 2024年冬号

future.

poweroflarge-scale

unlabeleddata.

arXiv:2401.10891 (2024)


ReferencesȏͳȐ Boguszewski, A., Batorski, D., ZiembaJankowska, N., Dziedzic, T., Zambrzycka, A.:
Landcover.

ai:

DatasetforȏͻȐ Arnold, L.L., Zandbergen, P.A.: Positional
accuracy of the wide area augmentation
system(2011)

roads from aerial imagery. In: Proceedings ofthe IEEE/CVF Conference on Computer

Vision and Pattern Recognition, pp. 1102–



1110 (2021)

ȏʹȐ Kumar, A., Kashiyama, T., Maeda, H., Omata,Sekimoto,Y.:

Real-time

citywidereconstruction of traffic flow from moving

cameras on lightweight edge devices. ISPRSJournal of Photogrammetry and Remote
Sensing 192, 115–129 (2022)
ȏ͵Ȑ Jocher, G., Chaurasia, A., Qiu, J.: Ultralytics
YOLOv8.

https://github.com/ultralytics/

ultralytics

ȏͶȐ Lin, T.-Y., Maire, M., Belongie, S., Hays, J.,
Perona, P., Ramanan, D., Doll´ar, P., Zitnick,

C.L.: Microsoft coco: Common objects incontext. In: Computer Vision–ECCV 2014:
13th

European

Switzerland,

Conference,

September

Zurich,

6-12,

2014,

Proceedings, Part V 13, pp. 740–755 (2014).
SpringerȏͷȐ Kendal, D.: Measuring distances using digital

cameras. Australian Senior Mathematics
Journal 21(2), 24–28 (2007)

ȏ͸Ȑ Karney, C.F.: Algorithms for geodesics.
Journal of Geodesy 87(1), 43–55 (2013)ȏ͹Ȑ Vincenty, T.: Direct and inverse solutions ofgeodesics on the ellipsoid with application ofnested equations. Survey Review 23(176),

88–93 (1975)
ȏͺȐ Yang, L., Kang, B., Huang, Z., Xu, X., Feng, J.,
Zhao, H.: Depth anything: Unleashing theconsumer-grade

gps

units.

Computers & Geosciences 37(7), 883–892

automatic

mapping of buildings, woodlands, water andH.,in