Trying to break new ground in aerial archaeology

Aerial reconnaissance continues to be a vital tool for landscape-oriented archaeological research. Although a variety of remote sensing platforms operate within the earth’s atmosphere, the majority of aerial archaeological information is still derived from oblique photographs collected during observer-directed reconnaissance flights, a prospection approach which has dominated archaeological aerial survey for the past century. The resulting highly biased imagery is generally catalogued in sub-optimal (spatial) databases, if at all, after which a small selection of images is orthorectified and interpreted. For decades, this has been the standard approach. Although many innovations, including digital cameras, inertial units, photogrammetry and computer vision algorithms, geographic(al) information systems and computing power have emerged, their potential has not yet been fully exploited in order to re-invent and highly optimise this crucial branch of landscape archaeology. The authors argue that a fundamental change is needed to transform the way aerial archaeologists approach data acquisition and image processing. By addressing the very core concepts of geographically biased aerial archaeological photographs and proposing new imaging technologies, data handling methods and processing procedures, this paper gives a personal opinion on how the methodological components of aerial archaeology, and specifically aerial archaeological photography, should evolve during the next decade if developing a more reliable record of our past is to be our central aim. In this paper, a possible practical solution is illustrated by outlining a turnkey aerial prospection system for total coverage survey together with a semi-automated back-end pipeline that takes care of photograph correction and image enhancement as well as the management and interpretative mapping of the resulting data products. In this way, the proposed system addresses one of many bias issues in archaeological research: the bias we impart to the visual record as a result of selective coverage. While the total coverage approach outlined here may not altogether eliminate survey bias, it can vastly increase the amount of useful information captured during a single reconnaissance flight while mitigating the discriminating effects of observer-based, on-the-fly target selection. Furthermore, the information contained in this paper should make it clear that with current technology it is feasible to do so. This can radically alter the basis for aerial prospection and move landscape archaeology forward, beyond the inherently biased patterns that are currently created by airborne archaeological prospection

Remote Sensing for Land Administration 2.0

The reprint “Land Administration 2.0” is an extension of the previous reprint “Remote Sensing for Land Administration”, another Special Issue in Remote Sensing. This reprint unpacks the responsible use and integration of emerging remote sensing techniques into the domain of land administration, including land registration, cadastre, land use planning, land valuation, land taxation, and land development. The title was chosen as “Land Administration 2.0” in reference to both this Special Issue being the second volume on the topic “Land Administration” and the next-generation requirements of land administration including demands for 3D, indoor, underground, real-time, high-accuracy, lower-cost, and interoperable land data and information

Performance Analysis of Different Optimization Algorithms for Multi-Class Object Detection

Object recognition is a significant approach employed for recognizing suitable objects from the image. Various improvements, particularly in computer vision, are probable to diagnose highly difficult tasks with the assistance of local feature detection methodologies. Detecting multi-class objects is quite challenging, and many existing researches have worked to enhance the overall accuracy. But because of certain limitations like higher network loss, degraded training ability, improper consideration of features, less convergent and so on. The proposed research introduced a hybrid convolutional neural network (H-CNN) approach to overcome these drawbacks. The collected input images are pre-processed initially through Gaussian filtering to eradicate the noise and enhance the image quality. Followed by image pre-processing, the objects present in the images are localized using Grid Guided Localization (GGL). The effective features are extracted from the localized objects using the AlexNet model. Different objects are classified by replacing the concluding softmax layer of AlexNet with Support Vector Regression (SVR) model. The losses present in the network model are optimized using the Improved Grey Wolf (IGW) optimization procedure. The performances of the proposed model are analyzed using PYTHON. Various datasets are employed, including MIT-67, PASCAL VOC2010, Microsoft (MS)-COCO and MSRC. The performances are analyzed by varying the loss optimization algorithms like improved Particle Swarm Optimization (IPSO), improved Genetic Algorithm (IGA), and improved dragon fly algorithm (IDFA), improved simulated annealing algorithm (ISAA) and improved bacterial foraging algorithm (IBFA), to choose the best algorithm. The proposed accuracy outcomes are attained as PASCAL VOC2010 (95.04%), MIT-67 dataset (96.02%), MSRC (97.37%), and MS COCO (94.53%), respectively

Evaluated density estimates of young forest stands using high resolution 2D imagery from UAV

After more than three decades of advanced technology and methods to evaluate young forest attributes, the forestry still prefers laborious field surveys in regenera-tion forests. Preceding studies have virtually all successfully identified the saplings, but the cost has outweighed the benefits or at least not yet convinced the companies to step away from the traditional field surveys; high-resolution photogrammetric point clouds and ALS-data can provide accurate estimations of the biophysical prop-erties but requires intense data processing. One way to reduce the costs could be to use less accurate 2D imagery by scaling images to the approximate altitude. The fundamentals of using cheap low-tech UAV imagery to aid management of young boreal forests was explored in this study, the results were obtained by com-paring image interpreted saplings from images processed by Dianthus Rapid Drone Map™ to Sveaskog’s traditional inventory practices of young forest stands. Three main issues were evaluated: ‘Camera positioning and image scale’, ‘Sapling classifi-cation’ and ‘Need for cleaning assessment’. The study area included 57 forest stands and 290 sample plots within the county of Västerbotten, northern Sweden. The ma-terial & methods in the study were mainly predetermined by available equipment and field instructions from Sveaskog to enable efficient data collection through combined labour- and research work. The results revealed that manual interpretation of the acquired images could dis-criminate between forest stands in need of cleaning and not with 82% accuracy, though the individual sapling counts comprised large errors, (Overall RMSE 6568 stems ha-1). Possible advantages compared to traditional surveys are the production of up to date high-quality maps for the brush-cutter operators, improved planning of retention delineation and most of all reduced time spent in field since over 1 million hectares are estimated in need of cleaning in Sweden annually. This study indicated that simple UAV imagery without proper photogrammetry could be an alternative; the quality might be bad but possibly sufficient.Efter mer än tre decennier av utveckling av ny teknik och avancerade metoder för att inventera ungskogar med hög precision, föredrar skogsbruket fortfarande att genom-föra tidskrävande fältundersökningar för ändamålet. Tidigare fjärranlysstudier har identifierat ungskogsstammar och röjningsbehov med bra resultat, men de har inte övertygat företagen att lämna de traditionella fältundersökningarna som uppfyller kraven på noggrannhet och hittills har varit konkurrenskraftiga i kostnadskalkylen. Högupplösta fotogrammetriska punktmoln och ALS-data kan ge noggranna uppskatt-ningar av ungskogens biofysiska egenskaper men kräver också intensiv databehand-ling. Ett sätt att minska kostnaderna kan vara att använda mindre exakta 2D-bilder genom att endast skala bilder till den approximerade flyghöjden. Denna studie har utvärderat grunderna för att använda enkla drönarbilder till in-ventering av boreala ungskogar och resultaten jämfördes med Sveaskogs nuvarande fältbaserade inventeringspraxis av unga skogsbestånd. Syftet var att utvärdera precis-ionen i tre huvudfrågor: ’Kamerans positionering och bildens skala’, Trädslags- och stamantals klassificering’ samt ’Bedömt röjningsbehov på beståndsnivå’. Studieom-rådet omfattade 57 bestånd och 290 provytor i närheten av Vindeln, Västerbottens län. Materialet i studien och det praktiska tillvägagångssättet bestämdes huvudsakli-gen utifrån tillgänglig utrustning och fältinstruktioner erhållna från Sveaskog. Resultaten visade att manuell tolkning av de förvärvade bilderna kunde skilja mel-lan skogsbestånd som behöver röjas och inte med 82% noggrannhet, även om tolkat antal stammar per provyta omfattade stora fel (t.ex. RMSE 6568 för totalt antal stam-mar ha-1). Möjliga fördelar jämfört med traditionella inventeringar är; framställning av uppdaterade kartor av hög kvalitet för röjningsarbetarna, förbättrad planering och avgränsning av hänsyn och framför allt minskad tid i fält eftersom över 1 miljon hek-tar beräknas vara i behov av röjning i Sverige årligen. Denna studie indikerar att enkla drönarbilder utan korrekt fotogrammetri kan vara ett alternativ till dagens manuella fältinventeringar. Kvalitén av bilderna kan vara låg men möjligen tillräcklig

Object-based mapping of temperate marine habitats from multi-resolution remote sensing data

PhD ThesisHabitat maps are needed to inform marine spatial planning but current methods of field survey and data interpretation are time-consuming and subjective. Object-based image analysis (OBIA) and remote sensing could deliver objective, cost-effective solutions informed by ecological knowledge. OBIA enables development of automated workflows to segment imagery, creating ecologically meaningful objects which are then classified based on spectral or geometric properties, relationships to other objects and contextual data. Successfully applied to terrestrial and tropical marine habitats for over a decade, turbidity and lack of suitable remotely sensed data had limited OBIA’s use in temperate seas to date. This thesis evaluates the potential of OBIA and remote sensing to inform designation, management and monitoring of temperate Marine Protected Areas (MPAs) through four studies conducted in English North Sea MPAs. An initial study developed OBIA workflows to produce circalittoral habitat maps from acoustic data using sequential threshold-based and nearest neighbour classifications. These methods produced accurate substratum maps over large areas but could not reliably predict distribution of species communities from purely physical data under largely homogeneous environmental conditions. OBIA methods were then tested in an intertidal MPA with fine-scale habitat heterogeneity using high resolution imagery collected by unmanned aerial vehicle. Topographic models were created from the imagery using photogrammetry. Validation of these models through comparison with ground truth measurements showed high vertical accuracy and the ability to detect decimetre-scale features. The topographic and spectral layers were interpreted simultaneously using OBIA, producing habitat maps at two thematic scales. Classifier comparison showed that Random Forests Abstract ii outperformed the nearest neighbour approach, while a knowledge-based rule set produced accurate results but requires further research to improve reproducibility. The final study applied OBIA methods to aerial and LiDAR time-series, demonstrating that despite considerable variability in the data, pre- and post-classification change detection methods had sufficient accuracy to monitor deviation from a background level of natural environmental fluctuation. This thesis demonstrates the potential of OBIA and remote sensing for large-scale rapid assessment, detailed surveillance and change detection, providing insight to inform choice of classifier, sampling protocol and thematic scale which should aid wider adoption of these methods in temperate MPAs.Natural Environment Research Council and Natural Englan

Smart environment monitoring through micro unmanned aerial vehicles

In recent years, the improvements of small-scale Unmanned Aerial Vehicles (UAVs) in terms of flight time, automatic control, and remote transmission are promoting the development of a wide range of practical applications. In aerial video surveillance, the monitoring of broad areas still has many challenges due to the achievement of different tasks in real-time, including mosaicking, change detection, and object detection. In this thesis work, a small-scale UAV based vision system to maintain regular surveillance over target areas is proposed. The system works in two modes. The first mode allows to monitor an area of interest by performing several flights. During the first flight, it creates an incremental geo-referenced mosaic of an area of interest and classifies all the known elements (e.g., persons) found on the ground by an improved Faster R-CNN architecture previously trained. In subsequent reconnaissance flights, the system searches for any changes (e.g., disappearance of persons) that may occur in the mosaic by a histogram equalization and RGB-Local Binary Pattern (RGB-LBP) based algorithm. If present, the mosaic is updated. The second mode, allows to perform a real-time classification by using, again, our improved Faster R-CNN model, useful for time-critical operations. Thanks to different design features, the system works in real-time and performs mosaicking and change detection tasks at low-altitude, thus allowing the classification even of small objects. The proposed system was tested by using the whole set of challenging video sequences contained in the UAV Mosaicking and Change Detection (UMCD) dataset and other public datasets. The evaluation of the system by well-known performance metrics has shown remarkable results in terms of mosaic creation and updating, as well as in terms of change detection and object detection

Service robotics and machine learning for close-range remote sensing

L'abstract è presente nell'allegato / the abstract is in the attachmen

Contribution to the application of near ground L-band radiometry

Premi HEMAV 2019 al millor TFGARIEL is an L-band radiometer adapted from Earth Observation satellite technology for use in terrestrial, near to ground surveys of moisture. The key technical benefits are compact size, lightweight, mobility and high pixel density (up to 1m2). This project demonstrates the capability of high spatial and temporal resolution L-Band radiometry to produce detailed soil moisture contour maps within a 1 km2 area. The study was performed prior, during and after 12 mm of rainfall to determine the soil surface absorption and adsorption behaviour in relation to surface moisture. The radiometer was equipped with photodiodes to enable the normalised difference vegetation index (NDVI) data to be extracted concurrently. Hence this is a very near ground, high resolution and high precision study of soil moisture derived from L-band emissivity. 
 The project is focused on the technology application and production of useful products in the form of moisture contour maps and vegetation detection. The radiometer functioned admirably during the consecutive test campaigns and in conditions that varied from direct sun to rain and mud. Patterns of soil moisture over time and within specific sub-areas of the field are identified and quantified. The intra-field differences appear to primarily be related to soil type and soil surface characteristics which were qualitatively assessed in this study as quantified approaches are available in empirical and theoretical studies. Average field moistures are measured daily and differentiation is made between soil types within the field. The effect of dry and moist surface emissivity on retrieved moisture is noted, as is the effect of vegetation on soil surface emissivity with the aid of the vegetation index. Comparisons are drawn to the highest resolution satellite imagery (30 m spatial, 3 day temporal) and highlight the limitations and richness of local data that is missed in relation to local soil moisture surface absorption patterns during rainfall. The radiometer is shown to achieve very high resolution and precision that is not possible from satellite or even light aircraft. Furthermore, it is shown to be able to study ground conditions when they are occluded from satellite and hence the moisture profile maps presented are unique in their detail.Award-winnin