Abstract
Context
The observation of the earth by humans has advanced our understanding of the physical patterns and processes that shape the landscape. Over time, the act of scientific interpretation has transformed into one mediated through machines, creating distance between the observer and the observed. Machine learning is expanding this gap and transforming how we gain knowledge about the world. Raising the question is there something to be lost by advancing machine learning at the expense of human visual interpretation?
Objectives
Recognizing the usefulness of these computational algorithms for dealing with massive, heterogeneous, and dynamic ecological datasets, scientists should not abandon the important contributions of human intelligence to understanding landscape patterns, processes, and relationships.
Methods
This paper presents a review of social, cultural, and political or military influences on the relationship between humans and remote sensing images of the landscape. This review highlights tensions between automated machine learning approaches and human interpretation.
Results
Support for the use of human–machine integrated systems through the use of interactive, visual display, and the development of transparent machine learning methods is suggested.
Conclusions
The human analyst should remain central in the design of landscape ecology applications when deploying machine learning algorithms. The complementary strengths of the human and machine in data processing suggest that the most informative insights regarding pattern and process can happen in the implementation of carefully designed Human in the Loop systems.
Similar content being viewed by others
References
Adamo M, Tarantino C, Tomaselli V, Kosmidou V, Petrou Z, Manakos I, Lucas RM, Mücher CA, Veronico G, Marangi C, De Pasquale V (2014) Expert knowledge for translating land cover/use maps to General Habitat Categories (GHC). Landsc Ecol 29:1045–1067
Ahlqvist O, Shortridge A (2010) Spatial and semantic dimensions of landscape heterogeneity. Landsc Ecol 25:573–590
Alpers S (1987) The mapping impulse in Dutch art Art and Cartography: Six Historical Essays. Chicago University Press, Chicago, pp 51–96
Andrienko G, Andrienko N, Jankowski P, Keim DA, Kraak M-J, MacEachren AM, Wrobel S (2007) Geovisual analytics for spatial decision support: setting the research agenda. Int J Geogr Inf Sci 21:839–858
Armstrong S, Sotala K (2015) How we’re predicting AI–or failing to. In: Romportl J, Zackova E, Kelemen J (eds) Beyond artificial intelligence. Springer, Cham, pp 11–29
Atkinson K (1995) Deville and photographic surveying. Photogramm Rec 15:189–195
Baldwin T (1785) Airopaidia: Containing the Narrative of a Balloon Excursion from Chester, the Eighth of September, Fletcher, J., London
Bianchetti RA (2016) Describing the problem-solving strategies of expert image interpreters using graphical knowledge elicitation methods. GISci Remote Sens 53:561–577
Bianchetti RA, MacEachren AM (2015) Cognitive themes emerging from air photo interpretation texts published to 1960. ISPRS Int J GeoInf 4:551–571
Billing B (2019) Circular visions: viewing the world from above in the late eighteenth century. J Hist Geogr 63:61–72
Bolander T (2019) Human vs machine intelligence. Proc Pragmat Constr 9:17–24
Bousquet A (2018) The eye of war: military perception from the telescope to the Drone. University of Minnesota Press, Minneapolis
Bugliarello G, Kern M, Schillinger AG (1989) Commercial remote-sensing satellites: adding transparency to the information age. Technol Soc 11:1–2
Campbell JB (2008) Origins of aerial photographic interpretation, US Army, 1916 to 1918. Photogramm Eng Remote Sens 74:77–93
Cloud J, Clarke KC (1999) Through a shutter darkly: the tangled relationships between civilian, military and intelligence remote sensing in the early US space program. In: Reppy J (ed) Secrecy and knowledge production. Cornell University, Ithaca, pp 36–56
Cohen WB, Yang Z, Kennedy R (2010) Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync—tools for calibration and validation. Remote Sens Environ 114:2911–2924
Colwell JE (1960) Manual of photographic interpretation. American Society of Photogrammetry, Herndon
Colwell RN (1965) The extraction of data from aerial photographs by human and mechanical means. Photogrammetria 20:211–228
Comber A, Fisher P, Wadsworth R (2015) Text mining analysis of land cover semantic overlap. In: Ahlqvist O, Varanka D, Fritz S (eds) Land use and land cover semantics: principles, best practices, and prospects. CRC Press, Boca Raton
Congress U (1987) Office of Technology Assessment (OTA), Commercial Newsgathering from Space—A Technical Memorandum. OTA-TM-ISC-40. US Government Printing Office [USGPO], Washington, DC
Crowley RS, Naus GJ, Stewart J, Friedman CP (2003) Development of visual diagnostic expertise in pathology-an information-processing study. J Am Med Inform Assoc 10:39–51
Daston L, Galison P (1992) The image of objectivity. Representations 40:81–128
Day D (2015) Eye in the sky: the story of the CORONA spy satellites. Smithsonian history of aviation and spaceflight, Smithsonian Institution, Washington, DC
Egenhofer M, Mark D (1995) Naive geography. In: Paper presented at the spatial information theory. A theoretical basis for GIS, Heidelberg
Florini AM (1988) The opening skies: third-party imaging satellites and US security. Int Secur 13:91–123
Giblett R (2012) Photography and landscape. Intellect, Bristol
Gobster PH, Nassauer JI, Daniel TC, Fry G (2007) The shared landscape: what does aesthetics have to do with ecology? Landsc Ecol 22:959–972
Hay G, Castilla G (2008) Geographic object-based image analysis (GEOBIA): a new name for a new discipline. In: Paper presented at the object-based image analysis, Calgary
Hayles NK (2016) Cognitive assemblages: technical agency and human interactions. Crit Inq 43:32–55
Hollan J, Hutchins E, Kirsh D (2000) Distributed cognition: toward a new foundation for human-computer interaction research. ACM Trans Comput Hum Interact 7:174–196
Huang AS-H, Lin Y-J (2019) The effect of landscape colour, complexity and preference on viewing behaviour. Landsc Res 45:214–227
Humphlett PE (1987) Mediasat: the use of remote-sensing satellites by news agencies. In: Library of Congress, Congressional Research Service
Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 20:1254–1259
Katja Grace JS, Allan Dafoe, Baobao Zhang, Owain Evans (2017) When will AI exceed human performance? Evidence from AI experts. J Artif Intell Res. arXiv:1705.08807v3
Krygier JB (1997) Envisioning the American West: maps, the representational barrage of 19th century expedition reports, and the production of scientific knowledge. Cartogr Geogr Inf Syst 24:27–50
Lansdale M, Underwood G, Davies C (2010) Something overlooked? How experts in change detection use visual saliency. Appl Cogn Psychol 24:213–225
Litfin KT (1997) The gendered eye in the sky: a feminist perspective on earth observation satellites. Frontiers 18:26–47
Lodder C (2013) Transfiguring reality: suprematism and the aerial view. In: Pousin F, Dorrian M (eds) Seeing from above. I.B. Tauris, London
Mack PE (1990) Viewing the earth: the social construction of the Landsat satellite system. Inside technology. MIT Press, Cambridge
Malevich K (2003) The non-objective world: the manifesto of suprematism. Dover Publications, Mineola
Matsuyama T (1987) Knowledge-based aerial image understanding systems and expert systems for image processing. IEEE Trans Geosci Remote Sens 3:305–316
Matzen LE, Haass MJ, Tran J, McNamara LA (2016) Using eye-tracking and saliency maps to assess image utility. In: Paper presented at the electronic imaging, human vision and electronic imaging
McKeown DM (1984) Knowledge-based aerial photo interpretation. Photogrammetria 39:91–123
Morgan JL, Gergel SE (2010) Quantifying historic landscape heterogeneity from aerial photographs using object-based analysis. Landsc Ecol 25:985–998
Newton AC, Hill RA, Echeverría C, Golicher D, Rey Benayas JM, Cayuela L, Hinsley SA (2009) Remote sensing and the future of landscape ecology. Prog Phys Geogr 33:528–546
Olson CE (1960) Elements of photographic interpretation common to several sensors. Photogramm Eng Remote Sens 26:651–656
Ooms K, Maeyer PD, Fack V (2015) Listen to the map user: cognition, memory, and expertise. Cartogr J 52:3–19
Parks L, Schwoch J (2012) Down to earth: satellite technologies, industries, and cultures. Rutgers University Press, New Brunswick
Perera AH, Drew CA, Johnson CJ (2012) Experts, expert knowledge, and their roles in landscape ecological applications. In: Perera AH, Drew CA, Johnson CJ (eds) Expert knowledge and its application in landscape ecology. Springer, New York, pp 1–10
Peters DP, Havstad KM, Cushing J, Tweedie C, Fuentes O, Villanueva-Rosales N (2014) Harnessing the power of big data: infusing the scientific method with machine learning to transform ecology. Ecosphere 5:1–15
Piaget J (2013) The child's conception of time. Routledge, New York
Quackenbush RS (1942) Photography: recording what the "eyes of the Fleet" see is vital ask of the aerial photographer. Ziff-Davis Publishing Company, Philadelphia
Rajbhandari S, Aryal J, Osborn J, Lucieer A, Musk R (2019) Leveraging machine learning to extend Ontology-Driven Geographic Object-Based Image Analysis (O-GEOBIA): a case study in forest-type mapping. Remote Sens 11:503
Rajbhandari S, Aryal J, Osborn J, Musk R, Lucieer A (2017) Benchmarking the applicability of ontology in geographic object-based image analysis. ISPRS Int J GeoInf 6:386
Rep. Fuqua D (1984) H.R. 4834 Land Remote Sensing Commercializtion Act of 1984. Science and Technology Committee, Washington DC
Saint-Amour PK (2003) Modernist reconnaissance. Mod Mod Proj MUSE 10:349–380
Scholes S (2018) How the government controls sensitive satellite data. Condé Nast, New York
Secretary TWHOotP (1994) PDD-23 Foreign Access to Remote Sensing Space Capabilities. United States, Washington, DC
Springer A, Whittaker S (2018) What are you hiding? Algorithmic transparency and user perceptions. In: 2018 association for the advancement of artificial intelligence AAAI spring symposium series, Palo Alto, CA, 2018, vol 4
Van Coillie FMB, Gardin S, Anseel F, Duyck W, Verbeke LPC, De Wulf RR (2014) Variability of operator performance in remote-sensing image interpretation: the importance of human and external factors. Int J Remote Sens 35:754–778
Virilio P (1994) The vision machine. Perspectives. Indiana University Press, Indianapolis
White AR (2019) Human expertise in the interpretation of remote sensing data: a cognitive task analysis of forest disturbance attribution. Int J Appl Earth Obs Geoinf 74:37–44
Zhen W, Yang L, Kwan MP, Zuo Z, Wan B, Zhou S, Li S, Ye Y, Qian H, Pan X (2020) Capturing what human eyes perceive a visual hierarchy generation approach to emulating saliency-based visual attention for grid-like urban street networks. Comput Environ Urban Syst 80:101454
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Portelli, R.A. Don’t throw the baby out with the bathwater: reappreciating the dynamic relationship between humans, machines, and landscape images. Landscape Ecol 35, 815–822 (2020). https://doi.org/10.1007/s10980-020-00992-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10980-020-00992-z