Vaš brskalnik ne omogoča JavaScript!
JavaScript je nujen za pravilno delovanje teh spletnih strani. Omogočite JavaScript ali pa uporabite sodobnejši brskalnik.
Nacionalni portal odprte znanosti
Odprta znanost
DiKUL
slv
|
eng
Iskanje
Brskanje
Novo v RUL
Kaj je RUL
V številkah
Pomoč
Prijava
Revising cadastral data on land boundaries using deep learning in image-based mapping
ID
Fetai, Bujar
(
Avtor
),
ID
Grigillo, Dejan
(
Avtor
),
ID
Lisec, Anka
(
Avtor
)
PDF - Predstavitvena datoteka,
prenos
(6,32 MB)
MD5: F0BE43CA0778524FBA69FF51EA04F1EF
URL - Izvorni URL, za dostop obiščite
https://www.mdpi.com/2220-9964/11/5/298
Galerija slik
Izvleček
One of the main concerns of land administration in developed countries is to keep the cadastral system up to date. The goal of this research was to develop an approach to detect visible land boundaries and revise existing cadastral data using deep learning. The convolutional neural network (CNN), based on a modified architecture, was trained using the Berkeley segmentation data set 500 (BSDS500) available online. This dataset is known for edge and boundary detection. The model was tested in two rural areas in Slovenia. The results were evaluated using recall, precision, and the F1 score—as a more appropriate method for unbalanced classes. In terms of detection quality, balanced recall and precision resulted in F1 scores of 0.60 and 0.54 for Ponova vas and Odranci, respectively. With lower recall (completeness), the model was able to predict the boundaries with a precision (correctness) of 0.71 and 0.61. When the cadastral data were revised, the low values were interpreted to mean that the lower the recall, the greater the need to update the existing cadastral data. In the case of Ponova vas, the recall value was less than 0.1, which means that the boundaries did not overlap. In Odranci, 21% of the predicted and cadastral boundaries overlapped. Since the direction of the lines was not a problem, the low recall value (0.21) was mainly due to overly fragmented plots. Overall, the automatic methods are faster (once the model is trained) but less accurate than the manual methods. For a rapid revision of existing cadastral boundaries, an automatic approach is certainly desirable for many national mapping and cadastral agencies, especially in developed countries.
Jezik:
Angleški jezik
Ključne besede:
geodesy
,
land
,
visible boundary
,
cadastre
,
maintenance
,
UAV
,
deep learning
Vrsta gradiva:
Članek v reviji
Tipologija:
1.01 - Izvirni znanstveni članek
Organizacija:
FGG - Fakulteta za gradbeništvo in geodezijo
Status publikacije:
Objavljeno
Različica publikacije:
Objavljena publikacija
Leto izida:
2022
Št. strani:
17 str.
Številčenje:
Vol. 11, iss. 5, art. 298
PID:
20.500.12556/RUL-137057
UDK:
528.4
ISSN pri članku:
2220-9964
DOI:
10.3390/ijgi11050298
COBISS.SI-ID:
107071235
Datum objave v RUL:
31.05.2022
Število ogledov:
757
Število prenosov:
148
Metapodatki:
Citiraj gradivo
Navadno besedilo
BibTeX
EndNote XML
EndNote/Refer
RIS
ABNT
ACM Ref
AMA
APA
Chicago 17th Author-Date
Harvard
IEEE
ISO 690
MLA
Vancouver
:
Kopiraj citat
Objavi na:
Gradivo je del revije
Naslov:
ISPRS international journal of geo-information
Skrajšan naslov:
ISPRS int. j. geo-inf.
Založnik:
MDPI
ISSN:
2220-9964
COBISS.SI-ID:
18678550
Licence
Licenca:
CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:
http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:
To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
Sekundarni jezik
Jezik:
Slovenski jezik
Ključne besede:
geodezija
,
zemljišče
,
vidna meja
,
kataster
,
vzdrževanje
,
UAV
,
globoko učenje
Projekti
Financer:
ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Številka projekta:
P2-0406
Naslov:
Opazovanje Zemlje in geoinformatika
Financer:
ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Številka projekta:
V2-1934
Naslov:
Ovrednotenje različnih načinov označitve katastrskih mejnikov za fotogrametrično izmero z letalnikom in analiza njihovega vpliva na položajno točnost oblaka točk in ortofota
Financer:
Drugi - Drug financer ali več financerjev
Program financ.:
Republic of Slovenia, Surveying and Mapping Authority
Podobna dela
Podobna dela v RUL:
Podobna dela v drugih slovenskih zbirkah:
Nazaj