1,720,982 research outputs found
2nd edition of instrumenting smart city applications with big sensing and earth observatory data: Tools, methods and techniques
Full text linkIn lieu of an abstract, this is an excerpt from the first page.
The exponential growth in the volume of Earth observation data and the increasing quality and availability of high-resolution imagery are increasingly making more applications possible in urban environments. In particular, remote sensing information, especially when combined with location-specific data collected locally or through connected devices, presents exciting opportunities for smart city applications, such as risk analysis and mitigation, climate prediction, and remote surveillance. On the other hand, the exploitation of this great amount of data poses new challenges for big data analysis models and requires new spatial information frameworks capable of integrating imagery, sensor observations, and social media in geographic information systems (GIS)
A Technical Note on AI-Driven Archaeological Object Detection in Airborne LiDAR Derivative Data, with CNN as the Leading Technique
Full text linkArchaeological research fundamentally relies on detecting features to uncover hidden historical information. Airborne (aerial) LiDAR technology has significantly advanced this field by providing high-resolution 3D terrain maps that enable the identification of ancient structures and landscapes with improved accuracy and efficiency. This technical note comprehensively reviews 45 recent studies to critically examine the integration of Machine Learning (ML) and Deep Learning (DL) techniques, particularly Convolutional Neural Networks (CNNs), with airborne LiDAR derivatives for automated archaeological feature detection. The review highlights the transformative potential of these approaches, revealing their capability to automate feature detection and classification, thus enhancing efficiency and accuracy in archaeological research. CNN-based methods, employed in 32 of the reviewed studies, consistently demonstrate high accuracy across diverse archaeological features. For example, ancient city walls were delineated with 94.12% precision using U-Net, Maya settlements with 95% accuracy using VGG-19, and with an IoU of around 80% using YOLOv8, and shipwrecks with a 92% F1-score using YOLOv3 aided by transfer learning. Furthermore, traditional ML techniques like random forest proved effective in tasks such as identifying burial mounds with 96% accuracy and ancient canals. Despite these significant advancements, the application of ML/DL in archaeology faces critical challenges, including the scarcity of large, labeled archaeological datasets, the prevalence of false positives due to morphological similarities with natural or modern features, and the lack of standardized evaluation metrics across studies. This note underscores the transformative potential of LiDAR and ML/DL integration and emphasizes the crucial need for continued interdisciplinary collaboration to address these limitations and advance the preservation of cultural heritage
Estimation of apparent thermal inertia of roofing materials from aerial thermal imagery
Full text linkThe rapid expansion of urban areas and soil sealing is enhancing the Urban Heat Island (UHI) phenomenon, especially during heat waves. The different thermal inertia of the building materials compared to natural surfaces is one of the major driving factors of UHI. The present contribution aims to test a methodology for mapping the Apparent Thermal Inertia (ATI)—a proxy that can be derived from remote sensing data—of roofing surfaces at the scale of an entire city and with a high spatial resolution. Day and night aerial thermal images with the resolution of 0.5 m were acquired over two test areas in Bologna (Italy), together with satellite multispectral data. Statistics on the buildings in the test areas are computed considering different classes of roofing materials (e.g. bituminous sheath, clay tiles, metal sheet, gravel tiles). Observed median ATI values for each class range from 0.03 to 0.09 K-1 with interquartile ranges between 0.02 and 0.14 K-1, so the intra-class variability in some cases appears higher than the variability among different material classes, proving the importance of ATI mapping for UHI investigations
Application of geostatistical analysis interacting with the earth observation data for recovery of raw materials from mining residuals (stockpiles and tailings): research projects at University of Bologna.
No full textThe poster presents an overview of the ongoing research projects at University of Bologna – DICAM Department, applying geostatistical methods to mining stockpiles and tailings with the purpose of metal recovery. The educational program RawMatCop of EIT Raw Materials is the main supporter of the research.
The work takes advantage of the use of Earth Observation (EO) data for sampling optimization in mining residuals from abandoned and active mines. Purposes are both recovery of raw materials and environmental rehabilitation of tailing dams and landfills. EO can play an important role in accounting the raw material resources of a territory, since current satellites, such as the Copernicus constellations (Sentinels), provide continuous spatial and temporal coverage of the global at no cost. Thanks to the spatial resolution, Copernicus data can improve the characterization (quantification and evaluation) of a resource, together with the assessment of the associated risks. Moreover, EO can be used for continuous monitoring of the target areas, conditioned by mining activites. On the other hand, geostatistical analysis, using in situ sampling and EO images, exploit innovative methods to improve accuracy of grade and pollution maps, thus reducing the number of samples, with evident cost reduction.
Test sites are bauxite residuals, located in Mediterranean Region: Greece and Montenegro (under analysis, 2019), Sardinia and Apulia (programmed work, 2020). Finally, a new international Project, INCO-Piles, starting in early 2020 and led by the research group, has the scope to identify the most promising mining residuals of Southern Europe for recovery of critical raw materials
Evaluation of Landsat-9 interoperability with Sentinel-2 and Landsat-8 over Europe and local comparison with field surveys
Full text linkThe recent launch of Landsat-9 satellite enriches the opportunities to work with dense time series of multispectral medium-resolution images. The integration of Landsat-9 in a multi-constellation series with Landsat-8 and Sentinel-2 requires a harmonization of the surface reflectance values that can be obtained from the official Level-2 products. This paper proposes the coefficients of the optimal linear transformations for the European continent, which allow to integrate Landsat-9 with the similar operating missions. These coefficients are based on a regression over 30 independent random extractions of 240,000 samples from images of the same areas but acquired by different sensors within two days. The coefficients were validated on an independent dataset. Furthermore, the effects of the proposed harmonization were tested on four popular vegetation indices, by evaluating the distributions of the differences in values obtained from each sensor pair. Finally, a test on a local scale was carried out with a spectroradiometer survey on 16 locations to collect some reference spectra to be compared with the reflectance values provided by the images. The results demonstrate the interoperability of Landsat and Sentinel-2 missions, since reflectance differences are in most cases within the accuracy specifications of the sensors. However, some discrepancies are observed in the blue and SWIR bands, probably due to inconsistencies in the atmospheric correction processes
Mobile data acquisition and processing in support of an urban heat island study
Full text linkGlobal warming and changes in Earth’s weather patterns are the main consequences of climate change, and bioclimate discomfort has significant public health problems, especially for the elderly. Normally, the thermal characteristics of urban areas are poor due to a phenomenon known as urban heat island. Mobile and fixed temperature measurements were performed on 19 March 2021 in the city of Bologna, Italy. Mobile measurements took place with a car, along a 75-km transect, starting at 22:16 with a duration of 2 hours and 41 minutes, while fixed measurements were done using 15 present weather stations and also placing five thermometers in the city center. Various interpolation models (i.e., Traditional, Voronoi Tessellation, Global Trends, Triangulated Irregular Networks, Inverse Distance Weighting and Kriging) were applied to correct the mobile measurements using fixed data. Kriging fulfilled the best result with a correlation coefficient of 0.99 compared to the raw temperatures
Remote sensing analysis of surface temperature from heterogeneous data in a maize field and related water stress
Full text linkPrecision agriculture aims at optimizing crop production by adapting management actions to real needs and requires that a reliable and extensive description of soil and crop conditions is available, that multispectral satellite images can provide. The purpose of the present study, based on activities carried out in 2019 on an agricultural area north of Ravenna (Italy) within the project LIFE AGROWETLANDS II, is to evaluate the potentials and limitations of freely available satellite thermal images for the identification of water stress conditions and the optimization of irrigation management practices, especially in agricultural areas and wetlands affected by saline soils and salt water capillary rise. Point field surveys and a very-high resolution thermal survey (5 cm) by an unmanned aerial vehicle (UAV) supported thermal camera were performed on a maize field tentatively at every Landsat-8 passage to check land surface temperature (LST) and canopy cover (CC) estimated from satellite. Temperature measured in the soil near ground surface and from UAV flying at 100 m altitude is compared with LST estimated from satellite measurements using three conversion methods: the top of atmosphere brightness temperature based on Landsat-8 band 10 (SB) corrected to account only for surface emissivity, the radiative transfer equation (RTE) for atmosphere effects correction, and the original split window method (SW) using both Thermal Infrared Sensor (TIRS) bands. The comparison shows discrepancies, due to extreme difference in resolution, the systematic hour of satellite passage (11 am solar time), and systematic differences between methods beside the unavoidable inaccuracy of UAV measurements. Satellite derived temperatures result usually lower than UAV measurements; SB produced the lowest values, SW the best (difference = -1.7 ± 1.7), and RTE the median (difference = -2.7 ± 1.6). The correlation between contemporary 30 m resolution temperature values of near pixels and corresponding tile-average temperatures was not significant, due to the purely numerical interpolation from the 100 m resolution TIRS images, whereas the time pattern along the season is consistent among methods, being correlation coefficient always greater than 0.85. Correlation coefficients among temperatures obtained from Landsat-8 by different methods are almost 1, showing that values are almost strictly related by a linear transformation. All the methods are useful to estimate water stress, since its associated Crop Water Stress Index (CWSI) is, from its definition, insensitive to linear transformation of temperatures. Actual evapotranspiration (ETa) maps are evaluated with the Surface Energy Balance Algorithm for Land (SEBAL) based on the three Landsat-8 derived LSTs; the higher is LST, the lower is ETa. Resulting ETa estimates are related with LST but not strictly, due to variation in vegetation cover and soil, therefore patterns result similar but not equivalent, whereas values are dependent on the atmosphere correction method. RTE and SW result in the best methods among the tested ones and the derived ETa values result reliable and appropriate to user needs. For real time application the Normalized Difference Moisture Index (NDMI), which can also be derived from more frequent Sentinel-2 passages, can be profitably used in combination or as a substitute of the CWSI
Multi-scale remote sensed Thermal Mapping of Urban Environments: approaches and issues
No full textAmong the applications of Remote Sensing for urban environments, Thermal Mapping is currently one of the most interesting, although still quite limited in usage. Airborne, UAV and satellite thermal imagery can in fact provide effective data for different purposes and at different scales. The peculiar characteristics of thermal images, on the other hand, make their use not really straightforward or immediate, and its insertion in an urban GIS must be carefully managed. The paper presents some approaches and solutions adopted for both the study of energy losses from buildings and the mapping of urban heat island
APPLICATION OF DEEP LEARNING CROP CLASSIFICATION MODEL BASED ON MULTISPECTRAL AND SAR SATELLITE IMAGERY
Full text linkClassifying crops using satellite data is a challenge, especially since most crops have similar growth cycles. Due to their different characteristics and chlorophyll content, different crops exhibit subtle differences in their reflectance spectra. This study uses a datadriven approach to build a series of deep learning models to classify 36 different land covers in Steele County and Traill Country, North Dakota, US. A Google Earth Engine workflow was implemented to generate a composite layer containing Sentinel 1 and Sentinel 2 satellite data and surface crop data over the study area. 200,000 sample points were generated on this layer, 140,000 for training dataset, 30,000 for validation dataset and 30,000 for testing dataset. Each sample point contains the values of 12 months of SAR and spectral data. In this way, a two-dimensional feature matrix of the time dimension and spectral band dimension (bands refer to specific wavelengths of data in remote sensing imagery and other type of data like NDVI) is generated for each sample point. The training dataset of the model is composed of the feature matrix of these sample points, and the surface crops as labels correspond to the feature matrix. Since this is a dataset with two-dimensional features, this research uses four deep learning models: Dense Neural Network (DNN), Long short-term memory (LSTM), Convolutional neural network (CNN) and Transformer. Among them, the Transformer model based on the self-attention mechanism performed the best, with a comprehensive accuracy rate of 85%, and the classification accuracy rate of crops with more than 2,000 sample points in the training data set reached more than 90%
GIS-Based Urban Heat Island Mapping and Analysis: Experiences in the City of Bologna
No full textClimate change effects have become increasingly visible recently through extreme weather events, such as heat waves. These are strictly related to a two-way relationship with urbanization. Indeed, urban expansion due to population migration from rural to urban areas impacts energy consumption, soil sealing with vegetation loss and gas emissions. Moreover, due to their characteristics, cities experience the typical urban heat island microclimate and are more vulnerable to heatwaves. In this context, having insight into the land surface temperature and accurate knowledge of city characteristics is essential to wise decision-making to ensure a more sustainable livelihood. The present paper provides an overview of two different approaches useful for thermal mapping at the city scale, implementing GIS-based analysis integrating local surveys with geospatial data. In particular, the city of Bologna (Italy) is studied. In the first study, temperature measurements along a transect was taken on March 19, 2021, with a mobile system. Then they were corrected considering data from some weather stations interpolated with Kriging, which shows the highest correlation coefficient of 0.99. The corrected temperature correlated at 0.69 with remote sensing NDVI data. The second study analyzed the significant impact of urban morphology, particularly building density, on temperature variations; it emphasizes the need for strategic urban planning to mitigate the Urban Heat Island effect
- …
