This work is licensed under the Creative Commons Attribution 4.0 International License.
Adamiak M., Będkowski K., Majchrowska A., 2021. Aerial imagery feature engineering using bidirectional generative adversarial networks: A case study of the Pilica River Region, Poland. Remote Sensing 13(2): 306. DOI 10.3390/rs13020306.AdamiakM.BędkowskiK.MajchrowskaA.2021Aerial imagery feature engineering using bidirectional generative adversarial networks: A case study of the Pilica River Region, Poland13230610.3390/rs13020306Open DOISearch in Google Scholar
Aslahishahri M., Stanley K.G., Duddu H., Shirtliffe S., Vail S., Bett K., Pozniak C., Stavness I., 2021. From RGB to NIR: Predicting of near infrared reflectance from visible spectrum aerial images of crops. In: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 1312–1322. DOI 10.1109/ICCVW54120.2021.00152.AslahishahriM.StanleyK.G.DudduH.ShirtliffeS.VailS.BettK.PozniakC.StavnessI.2021In:2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)1312132210.1109/ICCVW54120.2021.00152Open DOISearch in Google Scholar
Bagheri N., Ahmadi H., Alavi Panah S., Omid M., 2013. Multispectral remote sensing for site-specific nitrogen fertilizer management. Pesquisa Agropecuária Brasileira 48: 1394–1401. DOI 10.1590/S0100-204×2013001000011.BagheriN.AhmadiH.Alavi PanahS.OmidM.2013Multispectral remote sensing for site-specific nitrogen fertilizer management481394140110.1590/S0100-204×2013001000011Open DOISearch in Google Scholar
Barley A., Town C., 2014. Combinations of feature descriptors for texture image classification. Journal of Data Analysis and Information Processing 2(3): 67–76. DOI 10.4236/jdaip.2014.23009.BarleyA.TownC.2014Combinations of feature descriptors for texture image classification23677610.4236/jdaip.2014.23009Open DOISearch in Google Scholar
Barwiński M., 2009. Spatial development and functional changes in Łódź – Geographic, economic and political conditions. Geografia w szkole 6: 38–50.BarwińskiM.2009Spatial development and functional changes in Łódź – Geographic, economic and political conditions63850Search in Google Scholar
Będkowski K., Bielecki A., 2017. Assessment of the availability of greenery in the place of residence in cities using NDVI and the Lorenz's concentration curve. Teledetekcja Środowiska 57: 5–14.BędkowskiK.BieleckiA.2017Assessment of the availability of greenery in the place of residence in cities using NDVI and the Lorenz's concentration curve57514Search in Google Scholar
Chai T., Draxler R.R., 2014. Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geoscientific Model Development 7(3): 1247–1250. DOI 10.5194/gmd-7-1247-2014.ChaiT.DraxlerR.R.2014Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature731247125010.5194/gmd-7-1247-2014Open DOISearch in Google Scholar
Chew W.C., Hashim M., Lau A.M.S., Battay A.E., Kang C.S., 2014. Early detection of plant disease using close range sensing system for input into digital earth environment. IOP Conference Series: Earth and Environmental Science 18: 012143. DOI 10.1088/1755-1315/18/1/012143.ChewW.C.HashimM.LauA.M.S.BattayA.E.KangC.S.2014Early detection of plant disease using close range sensing system for input into digital earth environment1801214310.1088/1755-1315/18/1/012143Open DOISearch in Google Scholar
Chollet F., 2017. Xception: Deep learning with depthwise separable convolutions. arXiv:1610.02357 [cs], April. Online: http://arxiv.org/abs/1610.02357.CholletF.2017arXiv:1610.02357 [cs], April. Online: http://arxiv.org/abs/1610.02357.Search in Google Scholar
Davis C.H., Wang X., 2011. High-resolution DEMS for urban applications from NAPP photography. Photogrammetric Engineering and Remote Sensing 67: 4–11.DavisC.H.WangX.2011High-resolution DEMS for urban applications from NAPP photography67411Search in Google Scholar
Deering D., 1978. Rangeland reflectance characteristics measured by aircraft and spacecraft sensors. Thesis, Texas A&M University. Libraries. Online: https://oaktrust.library.tamu.edu/handle/1969.1/DISSERTATIONS-253780.DeeringD.1978Thesis,Texas A&M University. LibrariesOnline: https://oaktrust.library.tamu.edu/handle/1969.1/DISSERTATIONS-253780.Search in Google Scholar
Dematteis N., Giordan D., 2021. Comparison of digital image correlation methods and the impact of noise in geoscience applications. Remote Sensing 13(2): 327. DOI 10.3390/rs13020327.DematteisN.GiordanD.2021Comparison of digital image correlation methods and the impact of noise in geoscience applications13232710.3390/rs13020327Open DOISearch in Google Scholar
Demir U., Unal G., 2018. Patch-based image inpainting with generative adversarial networks. arXiv:1803.07422 [cs]. Online: http://arxiv.org/abs/1803.07422.DemirU.UnalG.2018arXiv:1803.07422 [cs]. Online: http://arxiv.org/abs/1803.07422.Search in Google Scholar
Donahue J., Simonyan K., 2019. Large scale adversarial representation learning. arXiv:1907.02544 [cs, stat]. Online: http://arxiv.org/abs/1907.02544.DonahueJ.SimonyanK.2019arXiv:1907.02544 [cs, stat]. Online: http://arxiv.org/abs/1907.02544.Search in Google Scholar
Dong J., Yin R., Sun X., Li Q., Yang Y., Qin X., 2019. Inpainting of remote sensing SST images with deep convolutional generative adversarial network. IEEE Geoscience and Remote Sensing Letters 16(2): 173–177. DOI 10.1109/LGRS.2018.2870880.DongJ.YinR.SunX.LiQ.YangY.QinX.2019Inpainting of remote sensing SST images with deep convolutional generative adversarial network16217317710.1109/LGRS.2018.2870880Open DOISearch in Google Scholar
EnviroSolutions Sp. z o.o. – Michał Włoga., 2021. Pobieracz danych GUGiK. Online: https://plugins.qgis.org/plugins/pobieracz_danych_gugik/.EnviroSolutions Sp. z o.o. – Michał Włoga2021Online: https://plugins.qgis.org/plugins/pobieracz_danych_gugik/.Search in Google Scholar
Geoportal, 2021. Online: http://geoportal.gov.pl.Geoportal2021Online: http://geoportal.gov.pl.Search in Google Scholar
Gu Y., Brown J., Verdin J., Wardlow B., 2007. A five-year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central great plains of the United States. Geophysical Research Letters 34(6). DOI 10.1029/2006GL029127.GuY.BrownJ.VerdinJ.WardlowB.2007A five-year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central great plains of the United States34610.1029/2006GL029127Open DOISearch in Google Scholar
Haralick R.M., Shanmugam K., Dinstein I., 1973. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics SMC 3(6): 610–621. DOI 10.1109/TSMC.1973.4309314.HaralickR.M.ShanmugamK.DinsteinI.1973Textural features for image classification3661062110.1109/TSMC.1973.4309314Open DOISearch in Google Scholar
Hatfield J., Prueger J., 2010. Value of using different vegetative indices to quantify agricultural crop characteristics at different growth stages under varying management practices. Remote Sensing 2(2): 562–578. DOI 10.3390/rs2020562.HatfieldJ.PruegerJ.2010Value of using different vegetative indices to quantify agricultural crop characteristics at different growth stages under varying management practices2256257810.3390/rs2020562Open DOISearch in Google Scholar
Head Office of Geodesy and Cartography., b.d. Integrated copies of databases of topographic objects. Główny Urząd Geodezji i Kartografii. Główny Urząd Geodezji i Kartografii. Online: https://www.geoportal.gov.pl/dane/baza-danych-obiektow-topograficznych-bdot (accessed 11 November 2020)Head Office of Geodesy and Cartographyb.d.Główny Urząd Geodezji i KartografiiOnline: https://www.geoportal.gov.pl/dane/baza-danych-obiektow-topograficznych-bdot (accessed 11 November 2020)Search in Google Scholar
Head Office of Geodesy and Cartography., b.d. Online: https://www.gov.pl/web/gugik-en (accessed 8 August 2022).Head Office of Geodesy and Cartographyb.d. Online: https://www.gov.pl/web/gugik-en (accessed 8 August 2022).Search in Google Scholar
Herold M., Liu X., Clarke K., 2003. Spatial metrics and image texture for mapping urban land use. Photogrammetric Engineering and Remote Sensing 69: 991–1001. DOI 10.14358/PERS.69.9.991.HeroldM.LiuX.ClarkeK.2003Spatial metrics and image texture for mapping urban land use69991100110.14358/PERS.69.9.991Open DOISearch in Google Scholar
Horé A., Ziou D., 2010. Image quality metrics: PSNR vs. SSIM. In: 2010 20th International Conference on Pattern Recognition, 2366–2369. DOI 10.1109/ICPR.2010.579.HoréA.ZiouD.2010In:2010 20th International Conference on Pattern Recognition2366236910.1109/ICPR.2010.579Open DOISearch in Google Scholar
Hunt E.R., Rock B., 1989. Detection of changes in leaf water content using near – And middle-infrared reflectances. Remote Sensing of Environment 30(1): 43–54. DOI 10.1016/0034-4257(89)90046-1.HuntE.R.RockB.1989Detection of changes in leaf water content using near – And middle-infrared reflectances301435410.1016/0034-4257(89)90046-1Open DOISearch in Google Scholar
Isola P., Zhu J-Y., Zhou T., Efros A., 2017. Image-to-image translation with conditional adversarial networks. arXiv:1611.07004 [cs], November 2021. Online: http://arxiv.org/abs/1611.07004.IsolaP.ZhuJ-Y.ZhouT.EfrosA.2017arXiv:1611.07004 [cs], November 2021. Online: http://arxiv.org/abs/1611.07004.Search in Google Scholar
Jackson R., Huete A., 1991. Interpreting vegetation indices. Preventive Veterinary Medicine 11(3): 185–200. DOI 10.1016/S0167-5877(05)80004-2.JacksonR.HueteA.1991Interpreting vegetation indices11318520010.1016/S0167-5877(05)80004-2Open DOISearch in Google Scholar
Jackson T., Chen M., Cosh M., Li F., Anderson M., Walthall C., Doriaswamy P., Ray Hunt R., 2004. Vegetation water content mapping using landsat data derived normalized difference water index for corn and soybeans. Remote Sensing of Environment, 2002 Soil Moisture Experiment (SMEX02), 92(4): 475–482. DOI 10.1016/j.rse.2003.10.021.JacksonT.ChenM.CoshM.LiF.AndersonM.WalthallC.DoriaswamyP.Ray HuntR.2004Vegetation water content mapping using landsat data derived normalized difference water index for corn and soybeans92447548210.1016/j.rse.2003.10.021Open DOISearch in Google Scholar
Jarocińska A., Zagajewski B., 2008. Correlations of ground – And airborne-level acquired vegetation indices of the Bystrzanka catchment. Teledetekcja Środowiska 40: 100–124.JarocińskaA.ZagajewskiB.2008Correlations of ground – And airborne-level acquired vegetation indices of the Bystrzanka catchment40100124Search in Google Scholar
Jung A., 2022. Imgaug. Python. Online: https://github.com/aleju/imgaug.JungA.2022PythonOnline: https://github.com/aleju/imgaug.Search in Google Scholar
Koza P., 2006. Orientation of Ikonos stereo images and automatic acquisition of height models. Archiwum Fotogrametrii, Kartografii i Teledetekcji 16. Online: http://yadda.icm.edu.pl/baztech/element/bwmeta1.element.baztech-3514d2c7-31a9-49d8-ad2d-c35825c950f8.KozaP.2006Orientation of Ikonos stereo images and automatic acquisition of height models16Online: http://yadda.icm.edu.pl/baztech/element/bwmeta1.element.baztech-3514d2c7-31a9-49d8-ad2d-c35825c950f8.Search in Google Scholar
Krukowski M., 2018. Modelowanie Kartograficzne w Ocenie Jakości Życia w Mieście – Aspekt Zieleni Miejskiej w Lublinie. Annales Universitatis Mariae Curie-Sklodowska, Sectio B – Geographia, Geologia, Mineralogia et Petrographia 73: 7–27. DOI 10.17951/b.2018.73.0.7-27.KrukowskiM.2018Modelowanie Kartograficzne w Ocenie Jakości Życia w Mieście – Aspekt Zieleni Miejskiej w Lublinie7372710.17951/b.2018.73.0.7-27Open DOISearch in Google Scholar
Krukowski M., Cebrykow P., Płusa J., 2016. Classification of green areas in Lublin based on satellite images Ikonos 2. Barometr Regionalny 14(2): 35–44.KrukowskiM.CebrykowP.PłusaJ.2016Classification of green areas in Lublin based on satellite images Ikonos 21423544Search in Google Scholar
Książek, J., 2018. Study of selected textural features properties on asbestos roof images. Geomatics and Environmental Engineering 12(4). DOI 10.7494/geom.2018.12.4.45.KsiążekJ.2018Study of selected textural features properties on asbestos roof images12410.7494/geom.2018.12.4.45Open DOISearch in Google Scholar
Kuang, W., Dou Y., 2020. Investigating the patterns and dynamics of urban green space in China's 70 major cities using satellite remote sensing. Remote Sensing 12(12): 1929. DOI 10.3390/rs12121929.KuangW.DouY.2020Investigating the patterns and dynamics of urban green space in China's 70 major cities using satellite remote sensing1212192910.3390/rs12121929Open DOISearch in Google Scholar
Kubalska J., Preuss R., 2014. Use of the photogrammetric data for vegetation inventory on urban areas. Archiwum Fotogrametrii, Kartografii i Teledetekcji 26: 75–86. DOI 10.14681/AFKIT.2014.006.KubalskaJ.PreussR.2014Use of the photogrammetric data for vegetation inventory on urban areas26758610.14681/AFKIT.2014.006Open DOISearch in Google Scholar
Łachowski W., Łęczek A., 2020. Tereny zielone w dużych miastach Polski. Analiza z wykorzystaniem Sentinel 2. Urban Development Issues 66(1): 77–90. DOI 10.51733/udi.2020.68.07.ŁachowskiW.ŁęczekA.2020Tereny zielone w dużych miastach Polski. Analiza z wykorzystaniem Sentinel 2661779010.51733/udi.2020.68.07Open DOISearch in Google Scholar
Li P., Cheng T., Guo J., 2009. Multivariate image texture by multivariate variogram for multispectral image classification. Photogrammetric Engineering & Remote Sensing 75(2): 147–157. DOI 10.14358/PERS.75.2.147.LiP.ChengT.GuoJ.2009Multivariate image texture by multivariate variogram for multispectral image classification75214715710.14358/PERS.75.2.147Open DOISearch in Google Scholar
Li X., Ratti C., 2018. Mapping the spatial distribution of shade provision of street trees in Boston using google street view Panoramas. Urban Forestry & Urban Greening 31: 109–119. DOI 10.1016/j.ufug.2018.02.013.LiX.RattiC.2018Mapping the spatial distribution of shade provision of street trees in Boston using google street view Panoramas3110911910.1016/j.ufug.2018.02.013Open DOISearch in Google Scholar
Marmol U., Lenda G., 2010. Texture filters in the process of automatic object classification. Archiwum Fotogrametrii, Kartografii i Teledetekcji 21: 235–243.MarmolU.LendaG.2010Texture filters in the process of automatic object classification21235243Search in Google Scholar
McPherson G., Xiao Q., van Doorn N., Johnson N., Albers S., Peper P., 2018. Shade factors for 149 taxa of in-leaf urban trees in the USA. Urban Forestry & Urban Greening 31: 204–211. DOI 10.1016/j.ufug.2018.03.001.McPhersonG.XiaoQ.van DoornN.JohnsonN.AlbersS.PeperP.2018Shade factors for 149 taxa of in-leaf urban trees in the USA3120421110.1016/j.ufug.2018.03.001Open DOISearch in Google Scholar
Mirza M., Osindero S., 2014. Conditional generative adversarial nets. arXiv:1411.1784 [cs, stat]. Online: http://arxiv.org/abs/1411.1784.MirzaM.OsinderoS.2014arXiv:1411.1784 [cs, stat]. Online: http://arxiv.org/abs/1411.1784.Search in Google Scholar
Müller M., Ekhtiari N., Almeida R., Rieke C., 2020. Super-resolution of multispectral satellite images using convolutional neural networks. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-1–2020 (August): 33–40. DOI 10.5194/isprs-annals-V-1-2020-33-2020.MüllerM.EkhtiariN.AlmeidaR.RiekeC.2020Super-resolution of multispectral satellite images using convolutional neural networksIn:V-1–2020August334010.5194/isprs-annals-V-1-2020-33-2020Open DOISearch in Google Scholar
Myeong S., Nowak D., Hopkins P., Brock R., 2003. Urban cover mapping using digital, high-resolution aerial imagery. Urban Ecosystems 5: 243–256. Online: http://www.fs.usda.gov/treesearch/pubs/18820.MyeongS.NowakD.HopkinsP.BrockR.2003Urban cover mapping using digital, high-resolution aerial imagery5243256Online: http://www.fs.usda.gov/treesearch/pubs/18820.Search in Google Scholar
Nowak D., Greenfield E., 2012. Tree and impervious cover change in U.S. cities. Urban Forestry & Urban Greening 11(1): 21–30. DOI 10.1016/j.ufug.2011.11.005.NowakD.GreenfieldE.2012Tree and impervious cover change in U.S. cities111213010.1016/j.ufug.2011.11.005Open DOISearch in Google Scholar
NumPy documentation. Online: https://numpy.org/doc/stable/reference/generated/numpy.savez.html (accessed 27 January 2022).Online: https://numpy.org/doc/stable/reference/generated/numpy.savez.html (accessed 27 January 2022).Search in Google Scholar
OpenCV., b.d. Online: https://opencv.org/ (accessed 27 January 2022)OpenCVb.d. Online: https://opencv.org/ (accessed 27 January 2022)Search in Google Scholar
Pluto-Kossakowska J., Władyka M., Tulkowska W., 2018. Assessment of remote sensing image data to identify objects in green and blue infrastructure. Teledetekcja Środowiska T 59. Online: http://yadda.icm.edu.pl/baztech/element/bwmeta1.element.baztech-9632f302-e255-497e-a9dd-368ea620f9b4.Pluto-KossakowskaJ.WładykaM.TulkowskaW.2018Teledetekcja ŚrodowiskaT 59. Online: http://yadda.icm.edu.pl/baztech/element/bwmeta1.element.baztech-9632f302-e255-497e-a9dd-368ea620f9b4.Search in Google Scholar
Pyra M., Adamczyk J., 2018. Object-oriented classification in the inventory of green infrastructure objects on the example of the Ursynów District in Warsaw. Teledetekcja Środowiska T. 59. Online: http://yadda.icm.edu.pl/baztech/element/bwmeta1.element.baztech-8bd759f8-2ab3-4b35-946d-b34b73f28b88.PyraM.AdamczykJ.2018Teledetekcja ŚrodowiskaT. 59. Online: http://yadda.icm.edu.pl/baztech/element/bwmeta1.element.baztech-8bd759f8-2ab3-4b35-946d-b34b73f28b88.Search in Google Scholar
Rouse J.W., Jr., Haas R.H., Schell J.A., Deering D.W., 1973. Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. Texas A&M Univ. College Station, TX, United States.RouseJ.W.Jr.HaasR.H.SchellJ.A.DeeringD.W.1973Texas A&M UnivCollege Station, TX, United StatesSearch in Google Scholar
Salimans T., Goodfellow I., Zaremba W., Cheung V., Radford A., Chen X., 2016. Improved techniques for training GANs. arXiv:1606.03498 [cs]. Online: http://arxiv.org/abs/1606.03498.SalimansT.GoodfellowI.ZarembaW.CheungV.RadfordA.ChenX.2016arXiv:1606.03498 [cs]. Online: http://arxiv.org/abs/1606.03498.Search in Google Scholar
Scikit-learn: Machine learning in Python – Scikit-learn 1.0.2 documentation., b.d. Online: https://scikit-learn.org/stable/ (accessed 27 January 2022)b.d. Online: https://scikit-learn.org/stable/ (accessed 27 January 2022)Search in Google Scholar
Small, C., 2001. Estimation of urban vegetation abundance by spectral mixture analysis. International Journal of Remote Sensing 22(7): 1305–1334. DOI 10.1080/01431160151144369.SmallC.2001Estimation of urban vegetation abundance by spectral mixture analysis2271305133410.1080/01431160151144369Open DOISearch in Google Scholar
Statistics Poland., 2020. Statistics of Łódź 2020. Lodz.Stat.Gov.Pl. Online: https://lodz.stat.gov.pl/en/publications/statistical-yearbook/statistics-of-lodz-2020,1,16.html.Statistics Poland2020Lodz.Stat.Gov.PlOnline: https://lodz.stat.gov.pl/en/publications/statistical-yearbook/statistics-of-lodz-2020,1,16.html.Search in Google Scholar
Suarez P., Sappa A., Vintimilla B., 2017. Learning image vegetation index through a conditional generative adversarial network. In: 2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM), 1–6. DOI 10.1109/ETCM.2017.8247538.SuarezP.SappaA.VintimillaB.2017In:2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM)1610.1109/ETCM.2017.8247538Open DOISearch in Google Scholar
Suárez P., Sappa A., Vintimilla B., Hammoud R., 2019. Image vegetation index through a cycle generative adversarial network. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 1014–1021. DOI 10.1109/CVPRW.2019.00133.SuárezP.SappaA.VintimillaB.HammoudR.2019In:2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)1014102110.1109/CVPRW.2019.00133Open DOISearch in Google Scholar
Sultana S., Ali A., Ahmad A., Mubeen M., Zia-Ul-Haq M., Ahmad S., Ercisli S., Jaafar H., 2014. Normalized difference vegetation index as a tool for wheat yield estimation: A case study from Faisalabad, Pakistan. The Scientific World Journal 2014: e725326. DOI 10.1155/2014/725326.SultanaS.AliA.AhmadA.MubeenM.Zia-Ul-HaqM.AhmadS.ErcisliS.JaafarH.2014Normalized difference vegetation index as a tool for wheat yield estimation: A case study from Faisalabad, Pakistan2014e72532610.1155/2014/725326Open DOISearch in Google Scholar
TensorFlow., (2018). 2022. TensorFlow documentation. Jupyter notebook. Tensorflow. Online: https://github.com/tensorflow/docs/blob/d58904052034c0870678709dc1ee8eb35e2fd34c/site/en/tutorials/generative/pix2pix.ipynb.TensorFlow2018TensorflowOnline: https://github.com/tensorflow/docs/blob/d58904052034c0870678709dc1ee8eb35e2fd34c/site/en/tutorials/generative/pix2pix.ipynb.Search in Google Scholar
TensorFlow Datasets., b.d. Online: https://www.tensorflow.org/datasets (accessed 27 January 2022)TensorFlow Datasetsb.d. Online: https://www.tensorflow.org/datasets (accessed 27 January 2022)Search in Google Scholar
Tomaszewska M., Lewiński S., Woźniak E., 2011. Use of MODIS satellite images to study the percentage of vegetation cover. Teledetekcja Środowiska 46: 15–22.TomaszewskaM.LewińskiS.WoźniakE.2011Use of MODIS satellite images to study the percentage of vegetation cover461522Search in Google Scholar
Tucker C., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8(2): 127–150. DOI 10.1016/0034-4257(79)90013-0.TuckerC.1979Red and photographic infrared linear combinations for monitoring vegetation8212715010.1016/0034-4257(79)90013-0Open DOISearch in Google Scholar
Turlej K., 2009. Comparison of NDVI index based on NOAA AVHRR, SPOT-VEGETATION and TERRA MODIS satellite data. Teledetekcja Środowiska 42: 83–88.TurlejK.2009Comparison of NDVI index based on NOAA AVHRR, SPOT-VEGETATION and TERRA MODIS satellite data428388Search in Google Scholar
Tuszynska J., Gatkowska M., Wrobel K., Jagiello K., 2018. A pilot study on determining approximate date of crop harvest on the basis of sentinel-2 satellite imagery. Geoinformation Issues 10(1): 65–77. Online: http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.baztech-46991614-3b5b-429e-892e-b1a2556684c5.TuszynskaJ.GatkowskaM.WrobelK.JagielloK.2018A pilot study on determining approximate date of crop harvest on the basis of sentinel-2 satellite imagery1016577Online: http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.baztech-46991614-3b5b-429e-892e-b1a2556684c5.Search in Google Scholar
van der Walt S., Schönberger JL., Nunez-Iglesias J., Boulogne F., Warner JD., Yager N., Gouillart E., Yu T., 2014. Scikit-image: Image processing in Python. PeerJ 2: e453. DOI 10.7717/peerj.453.van der WaltS.SchönbergerJL.Nunez-IglesiasJ.BoulogneF.WarnerJD.YagerN.GouillartE.YuT.2014Scikit-image: Image processing in Python2e45310.7717/peerj.453Open DOISearch in Google Scholar
Verykokou S., Ioannidis C., 2019. A Global Photogrammetry-Based Structure from Motion Framework: Application in Oblique Aerial Images. Conference paper: FIG Working Week 2019: Geospatial information for a smarter life and environmental resilience. Hanoi, VietnamVerykokouS.IoannidisC.2019Conference paper: FIG Working Week 2019: Geospatial information for a smarter life and environmental resilienceHanoi, VietnamSearch in Google Scholar
Wang Z., Bovik A., Sheikh H., Simoncelli E., 2004. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13(4): 600–612. DOI 10.1109/TIP.2003.819861.WangZ.BovikA.SheikhH.SimoncelliE.2004Image quality assessment: From error visibility to structural similarity13460061210.1109/TIP.2003.819861Open DOISearch in Google Scholar
Worm A., Będkowski K., Bielecki A., 2019. The use of surface and volume indicators from high resolution remote sensing data to assess the vegetation filling of urban quarters in Łódź City Centre, Poland. Teledetekcja Środowiska T. 60. Online: http://yadda.icm.edu.pl/baztech/element/bwmeta1.element.baztech-4a024b76-0072-48be-94a6-ceea9e001322.WormA.BędkowskiK.BieleckiA.2019Teledetekcja ŚrodowiskaT. 60. Online: http://yadda.icm.edu.pl/baztech/element/bwmeta1.element.baztech-4a024b76-0072-48be-94a6-ceea9e001322.Search in Google Scholar
Yao G., Yilmaz A., Zhang L., Meng F., Ai H., Jin F., 2021. Matching large baseline oblique stereo images using an end-to-end convolutional neural network. Remote Sensing 13(2): 274. DOI 10.3390/rs13020274.YaoG.YilmazA.ZhangL.MengF.AiH.JinF.2021Matching large baseline oblique stereo images using an end-to-end convolutional neural network13227410.3390/rs13020274Open DOISearch in Google Scholar
Zhang Y., 2001. Texture-integrated classification of urban treed areas in high-resolution color-infrared imagery. Photogrammetric Engineering & Remote Sensing 67(12): 1359–1365.ZhangY.2001Texture-integrated classification of urban treed areas in high-resolution color-infrared imagery671213591365Search in Google Scholar
Zhou S., Gordon M., Krishna R., Narcomey A., Fei-Fei L., Bernstein M., 2019. HYPE: a benchmark for human eYe perceptual evaluation of generative models. arXiv:1904.01121 [cs]. Online: http://arxiv.org/abs/1904.01121.ZhouS.GordonM.KrishnaR.NarcomeyA.Fei-FeiL.BernsteinM.2019arXiv:1904.01121 [cs]. Online: http://arxiv.org/abs/1904.01121.Search in Google Scholar
Zięba-Kulawik K., Hawryło P., Wężyk P., Matczak P., Przewoźna P., Inglot A., Mączka K., 2021. Improving methods to calculate the loss of ecosystem services provided by urban trees using LiDAR and aerial orthophotos. Urban Forestry & Urban Greening 63(sierpień): 127195. DOI 10.1016/j.ufug.2021.127195.Zięba-KulawikK.HawryłoP.WężykP.MatczakP.PrzewoźnaP.InglotA.MączkaK.2021Improving methods to calculate the loss of ecosystem services provided by urban trees using LiDAR and aerial orthophotos63sierpień12719510.1016/j.ufug.2021.127195Open DOISearch in Google Scholar
Zięba-Kulawik K., Wężyk P., 2022. Monitoring 3D changes in urban forests using landscape metrics analyses based on multi-temporal remote sensing data. Land 11(6): 883. DOI 10.3390/land11060883.Zięba-KulawikK.WężykP.2022Monitoring 3D changes in urban forests using landscape metrics analyses based on multi-temporal remote sensing data11688310.3390/land11060883Open DOISearch in Google Scholar