New Computational Methods for Automated Large-Scale Archaeological Site Detection

Aquesta tesi doctoral presenta una sèrie d'enfocaments, fluxos de treball i models innovadors en el camp de l'arqueologia computacional per a la detecció automatitzada a gran escala de jaciments arqueològics. S'introdueixen nous conceptes, enfocaments i estratègies, com ara lidar multitemporal, aprenentatge automàtic híbrid, refinament, curriculum learning i blob analysis; així com diferents mètodes d'augment de dades aplicats per primera vegada en el camp de l'arqueologia. S'utilitzen múltiples fonts, com ara imatges de satèl·lits multiespectrals, fotografies RGB de plataformes VANT, mapes històrics i diverses combinacions de sensors, dades i fonts. Els mètodes creats durant el desenvolupament d'aquest doctorat s'han avaluat en projectes en curs: Urbanització a Hispània i la Gàl·lia Mediterrània en el primer mil·lenni aC, detecció de monticles funeraris utilitzant algorismes d'aprenentatge automàtic al nord-oest de la Península Ibèrica, prospecció arqueològica intel·ligent basada en drons (DIASur), i cartografiat del patrimoni arqueològic al sud d'Àsia (MAHSA), per a la qual s'han dissenyat fluxos de treball adaptats als reptes específics del projecte. Aquests nous mètodes han aconseguit proporcionar solucions als problemes comuns d'estudis arqueològics presents en estudis similars, com la baixa precisió en detecció i les poques dades d'entrenament. Els mètodes validats i presentats com a part de la tesi doctoral s'han publicat en accés obert amb el codi disponible perquè puguin implementar-se en altres estudis arqueològics.Esta tesis doctoral presenta una serie de enfoques, flujos de trabajo y modelos innovadores en el campo de la arqueología computacional para la detección automatizada a gran escala de yacimientos arqueológicos. Se introducen nuevos conceptos, enfoques y estrategias, como lidar multitemporal, aprendizaje automático híbrido, refinamiento, curriculum learning y blob analysis; así como diferentes métodos de aumento de datos aplicados por primera vez en el campo de la arqueología. Se utilizan múltiples fuentes, como lidar, imágenes satelitales multiespectrales, fotografías RGB de plataformas VANT, mapas históricos y varias combinaciones de sensores, datos y fuentes. Los métodos creados durante el desarrollo de este doctorado han sido evaluados en proyectos en curso: Urbanización en Iberia y la Galia Mediterránea en el Primer Milenio a. C., Detección de túmulos mediante algoritmos de aprendizaje automático en el Noroeste de la Península Ibérica, Prospección Arqueológica Inteligente basada en Drones (DIASur), y cartografiado del Patrimonio del Sur de Asia (MAHSA), para los que se han diseñado flujos de trabajo adaptados a los retos específicos del proyecto. Estos nuevos métodos han logrado proporcionar soluciones a problemas comunes de la prospección arqueológica presentes en estudios similares, como la baja precisión en detección y los pocos datos de entrenamiento. Los métodos validados y presentados como parte de la tesis doctoral se han publicado en acceso abierto con su código disponible para que puedan implementarse en otros estudios arqueológicos.This doctoral thesis presents a series of innovative approaches, workflows and models in the field of computational archaeology for the automated large-scale detection of archaeological sites. New concepts, approaches and strategies are introduced such as multitemporal lidar, hybrid machine learning, refinement, curriculum learning and blob analysis; as well as different data augmentation methods applied for the first time in the field of archaeology. Multiple sources are used, such as lidar, multispectral satellite imagery, RGB photographs from UAV platform, historical maps, and several combinations of sensors, data, and sources. The methods created during the development of this PhD have been evaluated in ongoing projects: Urbanization in Iberia and Mediterranean Gaul in the First Millennium BC, Detection of burial mounds using machine learning algorithms in the Northwest of the Iberian Peninsula, Drone-based Intelligent Archaeological Survey (DIASur), and Mapping Archaeological Heritage in South Asia (MAHSA), for which workflows adapted to the project’ s specific challenges have been designed. These new methods have managed to provide solutions to common archaeological survey problems, presented in similar large-scale site detection studies, such as the low precision in previous detection studies and how to handle problems with few training data. The validated approaches for site detection presented as part of the PhD have been published as open access papers with freely available code so can be implemented in other archaeological studies

Wykorzystanie danych termalnych pozyskanych z pułapu lotniczego do określania stanu zdrowotnego wybranych gatunków drzew

Celem pracy było sprawdzenie, czy dane termalne z zakresu średniej podczerwieni (3,6–4,9 μm) pozyskane z pułapu lotniczego mogą być wykorzystane do badań kondycji zdrowotnej drzew. W tym celu przeprowadzono trzy analizy na niezależnych zbiorach danych w różnych środowiskach. Badania wykonane na danych termalnych pozyskanych w ciągu dnia wykazały, że temperatura korony jest cechą specyficzną dla gatunku i zależy od położenia drzewa w terenie. Drzewa znajdujące się wewnątrz lasu miały niższą temperaturę koron do 0,70oC niż te rosnące poza lasem. Gatunkiem o najwyższej temperaturze, niezależnie od godziny pozyskania danych lotniczych, był Pinus sylvestris. Niskimi temperaturami charakteryzowały się Alnus glutinosa, Quercus rubra i Quercus petraea. Badania nad identyfikacją miejsc żerowania kornika drukarza wykazały, że fuzja danych termalnych i skanowania laserowego umożliwiły wyznaczenie temperatury koron pojedynczych drzew Picea abies i sklasyfikowanie ich do trzech klas zdrowotnych (drzewa 'zdrowe' o średniej temperaturze 27,70oC; 'o osłabionej kondycji' 28,57oC i 'martwe' 30,17oC). Opracowany został schemat postępowania wykorzystujący automatyczną segmentację i uczenie maszynowe do identyfikacji drzew 'o osłabionej kondycji' i 'martwych'. Badania przeprowadzone w środowisku miejskim wykazały statystycznie istotne różnice między klasami kondycji zdrowotnej drzew zarówno na danych pozyskanych w dzień jak i w nocy. Korony drzew zdrowych były chłodniejsze w porównaniu do koron drzew zamierających. Średnia wartość różnicy wynosiła 3,28oC w ciągu dnia oraz 1,06oC w nocy. Podsumowując, lotnicze dane termalne z zakresu średniej podczerwieni mogą być wykorzystane do badań kondycji zdrowotnej wybranych gatunków drzew. Zmienność temperatur koron jest cechą zależną od gatunku i może być wskaźnikiem stanu zdrowotnego w środowisku naturalnym i miejskim."InterDOC-STARt – Interdyscyplinarne Studia Doktoranckie na Wydziale BiOŚ UŁ” – Program Operacyjny Wiedza Edukacja Rozwój 2014-2020, Oś priorytetowa III. Szkolnictwo wyższe dla gospodarki i rozwoju, Działanie 3.2 Studia doktoranckie. Nr projektu: POWR.03.02.00-IP.08-00-DOK/16. Realizowany w latach 2018-2022

Guidance for benthic habitat mapping: an aerial photographic approach

This document, Guidance for Benthic Habitat Mapping: An Aerial Photographic Approach, describes proven technology that can be applied in an operational manner by state-level scientists and resource managers. This information is based on the experience gained by NOAA Coastal Services Center staff and state-level cooperators in the production of a series of benthic habitat data sets in Delaware, Florida, Maine, Massachusetts, New York, Rhode Island, the Virgin Islands, and Washington, as well as during Center-sponsored workshops on coral remote sensing and seagrass and aquatic habitat assessment. (PDF contains 39 pages) The original benthic habitat document, NOAA Coastal Change Analysis Program (C-CAP): Guidance for Regional Implementation (Dobson et al.), was published by the Department of Commerce in 1995. That document summarized procedures that were to be used by scientists throughout the United States to develop consistent and reliable coastal land cover and benthic habitat information. Advances in technology and new methodologies for generating these data created the need for this updated report, which builds upon the foundation of its predecessor

OpenStreetMap – building data completeness visualization in terms of “Fitness for purpose”

The purpose of this article was to provide the user with information about the number of buildings in the analyzed OpenStreetMap (OSM) dataset in the form of data completeness indicators, namely the standard OSM building areal completeness index (C Index), the numerical completeness index (COUNT Index) and OSM building location accuracy index (TP Index). The official Polish vector database BDOT10k (Database of Topographic Objects) was designated as the reference dataset. Analyses were carried out for Piaseczno County in Poland, differentiated by land cover structure and urbanization level. The results were presented in the form of a bivariate choropleth map with an individually selected class interval suitable for the statistical distribution of the analyzed data. The results confirm that the completeness of OSM buildings close to 100% was obtained mainly in built-up areas. Areas with a commission of OSM buildings were distinguished in terms of area and number of buildings. Lower values of completeness rates were observed in less urbanized areas. The developed methodology for assessing the quality of OSM building data and visualizing the quality results to assist the user in selecting a dataset is universal and can be applied to any OSM polygon features, as well as for peer review of other spatial datasets of comparable thematic scope and detail

BVLOS UAV missions for vegetation mapping in maritime Antarctic

Polar areas are among the regions where climate change occurs faster than on most of the other areas on Earth. To study the effects of climate change on vegetation, there is a need for knowledge on its current status and properties. Both classic field observation methods and remote sensing methods based on manned aircraft or satellite image analysis have limitations. These include high logistic operation costs, limited research areas, high safety risks, direct human impact, and insufficient resolution of satellite images. Fixed-wing unmanned aerial vehicle beyond the visual line of sight (UAV BVLOS) missions can bridge the scale gap between field-based observations and full-scale airborne or satellite surveys. In this study the two operations of the UAV BVLOS, at an altitude of 350m ASL, have been successfully performed in Antarctic conditions. Maps of the vegetation of the western shore of Admiralty Bay (King George Island, South Shetlands, Western Antarctic) that included the Antarctic Specially Protected Area No. 128 (ASPA 128) were designed. The vegetation in the 7.5 km2 area was mapped in ultra-high-resolution(<5cm and DEM of 0.25m GSD), and from the Normalized Difference Vegetation Index (NDVI), four broad vegetation units were extracted: “dense moss carpets” (covering 0.14 km2 ,0.8%ofASPA128), “Sanionia uncinata moss bed” (0.31 km2 , 1.7% of ASPA 128), “Deschampsia antarctica grass meadow” (0.24 km2,1.3% of ASPA 128), and “Deschampsia antarctica–Usnea antarctica heath” (1.66 km2,9.4% of ASPA 128). Our results demonstrate that the presented UAV BVLOS–based surveys are time-effective (single flight lasting 2.5 h on a distance of 300 km) and cost-effective when compared to classical field-based observations and are less invasive for the ecosystem. Moreover, unmanned airborne vehicles significantly improve security, which is of particular interest in polar region research. Therefore, their development is highly recommended for monitoring areas in remote and fragile environments. KEYWORD

Earth Resources: A continuing bibliography with indexes, issue 16, January 1978

This bibliography lists 543 reports, articles, and other documents introduced onto the NASA scientific and technical information system between October 1 and December 31, 1977. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Agricultural land systems : modelling past, present and future regional dynamics

This thesis arises from the understanding of how the integration of concepts, tools, techniques, and methods from geographic information science (GIS) can provide a formalised knowledge base for agricultural land systems in response to future agricultural and food system challenges. To that end, this thesis focuses on understanding the potential application of GIS-based approaches and available spatial data sources for modelling regional agricultural land-use and production dynamics in Portugal. The specific objectives of this thesis are addressed in seven chapters in Parts II through V, each corresponding to one scientific article that was either published or is being considered for publication in peer-reviewed international scientific journals. In Part II, Chapter 2 summarises the body of knowledge and provides the context for the contribution of this thesis within the scientific domain of agricultural land systems. In Part III, Chapters 3 and 4 explore remotely sensed and Volunteered Geographic Information (VGI) data, multitemporal and multisensory approaches, and a variety of statistical methods for mapping, quantifying, and assessing regional agricultural land dynamics in the Beja district. In Part IV, Chapters 5–7 explore the CA-Markov model, Markov chain model, machine learning, and model-agnostic approach, as well as a set of spatial metrics and statistical methods for modelling the factors and spatiotemporal changes of agricultural land use in the Beja district. In Part V, Chapter 8 explores an area-weighting GIS-based technique, a spatiotemporal data cube, and statistical methods to model the spatial distribution across time for regional agricultural production in Portugal. The case studies in the thesis contribute practical and theoretical knowledge by demonstrating the strengths and limitations of several GIS-based approaches. Together, the case studies demonstrate the underlying principles that underpin each approach in a way that allows us to infer their potentiality and appropriateness for modelling regional agricultural land-use and production dynamics, stimulating further research along this line. Generally, this thesis partly reflects the state-of-art of land-use modelling and contribute significantly to the introduction of advances in agricultural system modelling research and land-system science