Search Results (281)

Human hair, commonly considered a discarded organic waste, is a keratin-rich material with remarkable potential for sustainable agriculture as an innovative resource. This study systematically explored the potential of non-composted human hair hydrolysates as eco-friendly and nutrient-rich liquid fertilizers, emphasizing their ability to enhance agricultural sustainability and mitigate organic waste accumulation. Eight distinct hydrolysates prepared with alkaline solutions were evaluated for their effects on plant growth using red-hot chili pepper (Capsicum frutescens) as the primary model under greenhouse conditions. The present study introduces a novel approach by employing an advanced digital image analysis technique to quantitatively assess 37 distinct plant growth parameters, providing an unprecedented depth of understanding regarding the impact of liquid human hair hydrolysates on plant development. Additionally, the integration of pilot-scale field trials and multi-species evaluations highlights the broader applicability and scalability of these hydrolysates as sustainable fertilizers. Collectively, these features establish this research as a pioneering contribution to sustainable agriculture and bio-waste management. The top-performing hydrolysates (KCaMgN, KMgN, KCaN) demonstrated significant enhancements in plant growth metrics, with fresh weight reaching up to 3210 mg, projected leaf area of approximately 132 cm², and crown diameter of 20.91 cm for the best-performing formulations, outperforming a commercial organomineral fertilizer by 20–46% in overall growth performance. Furthermore, observational studies on various species (such as bird of paradise flower (Strelitzia reginae), avocado (Persea americana), lemon (Citrus limon L.), Mazafati date (Phoenix dactylifera L.), and red mini conical hot pepper (Capsicum annuum var. conoides) and field trials on long sweet green peppers (Capsicum annuum) confirmed the broad applicability of these hydrolysates. Toxicity assessments using shortfin molly fish (Poecilia sphenops) validated the environmental safety of plants cultivated with hydrolysates. These findings highlight that human hair hydrolysates offer a sustainable alternative to synthetic fertilizers, contributing to waste management efforts while enhancing agricultural productivity. Full article

The growth of photovoltaic (PV) installations is essential for the global energy transition; however, comprehensive data regarding their spatial distribution are limited, which complicates effective energy planning. This research introduces a methodology for automatic recognition of ground-mounted PV systems in Italy, using semantic segmentation and Sentinel-2 RGB images with a resolution of 10 m. The objective of this methodology is to accurately identify both the locations and the sizes of these installations, estimate their capacity, and facilitate regular updates to maps, thereby supporting energy planning strategies. The segmentation model, which is founded on a U-Net architecture, is trained using a dataset from 2019 and evaluated on two separate cases that involve different dates and geographical areas. We propose a multi-temporal approach, applying the model to a sequence of images taken throughout the year and aggregating the results to create a PV detection probability map. Users have the flexibility to modify probability thresholds to enhance accuracy: lower thresholds increase producer accuracy, ensuring continuous area detection for capacity estimation, while higher thresholds boost user accuracy by reducing false positives. Additionally, post-processing techniques, such as filtering for plastic-covered greenhouses, assist minimizing detection errors. However, there is a need for improved model generalizability across various landscapes, necessitating retraining with images from a range of environmental contexts. Full article

To date, there is little publicly available scientific data on unidentified aerial phenomena (UAP) whose properties and kinematics purportedly reside outside the performance envelope of known phenomena. To address this deficiency, the Galileo Project is designing, building, and commissioning a multi-modal, multi-spectral ground-based observatory to continuously monitor the sky and collect data for UAP studies via a rigorous long-term aerial census of all aerial phenomena, including natural and human-made. One of the key instruments is an all-sky infrared camera array using eight uncooled long-wave-infrared FLIR Boson 640 cameras. In addition to performing intrinsic and thermal calibrations, we implement a novel extrinsic calibration method using airplane positions from Automatic Dependent Surveillance–Broadcast (ADS-B) data that we collect synchronously on site. Using a You Only Look Once (YOLO) machine learning model for object detection and the Simple Online and Realtime Tracking (SORT) algorithm for trajectory reconstruction, we establish a first baseline for the performance of the system over five months of field operation. Using an automatically generated real-world dataset derived from ADS-B data, a dataset of synthetic 3D trajectories, and a hand-labeled real-world dataset, we find an acceptance rate (fraction of in-range airplanes passing through the effective field of view of at least one camera that are recorded) of 41% for ADS-B-equipped aircraft, and a mean frame-by-frame aircraft detection efficiency (fraction of recorded airplanes in individual frames which are successfully detected) of 36%. The detection efficiency is heavily dependent on weather conditions, range, and aircraft size. Approximately 500,000 trajectories of various aerial objects are reconstructed from this five-month commissioning period. These trajectories are analyzed with a toy outlier search focused on the large sinuosity of apparent 2D reconstructed object trajectories. About 16% of the trajectories are flagged as outliers and manually examined in the IR images. From these ∼80,000 outliers and 144 trajectories remain ambiguous, which are likely mundane objects but cannot be further elucidated at this stage of development without information about distance and kinematics or other sensor modalities. We demonstrate the application of a likelihood-based statistical test to evaluate the significance of this toy outlier analysis. Our observed count of ambiguous outliers combined with systematic uncertainties yields an upper limit of 18,271 outliers for the five-month interval at a 95% confidence level. This test is applicable to all of our future outlier searches. Full article

Geophysical surveys, a means of analyzing the Earth and its environments, have traditionally relied on ground-based methodologies. However, up-to-date approaches encompass remote sensing (RS) techniques, employing both spaceborne and airborne platforms. The emergence of Unmanned Aerial Vehicles (UAVs) has notably catalyzed interest in UAV-borne geophysical RS. The objective of this study is to comprehensively review the state-of-the-art UAV-based geophysical methods, encompassing magnetometry, gravimetry, gamma-ray spectrometry/radiometry, electromagnetic (EM) surveys, ground penetrating radar (GPR), traditional UAV RS methods (i.e., photogrammetry and LiDARgrammetry), and integrated approaches. Each method is scrutinized concerning essential aspects such as sensors, platforms, challenges, applications, etc. Drawing upon an extensive systematic review of over 435 scholarly works, our analysis reveals the versatility of these systems, which ranges from geophysical development to applications over various geoscientific domains. Among the UAV platforms, rotary-wing multirotors were the most used (64%), followed by fixed-wing UAVs (27%). Unmanned helicopters and airships comprise the remaining 9%. In terms of sensors and methods, imaging-based methods and magnetometry were the most prevalent, which accounted for 35% and 27% of the research, respectively. Other methods had a more balanced representation (6–11%). From an application perspective, the primary use of UAVs in geoscience included soil mapping (19.6%), landslide/subsidence mapping (17.2%), and near-surface object detection (13.5%). The reviewed studies consistently highlight the advantages of UAV RS in geophysical surveys. UAV geophysical RS effectively balances the benefits of ground-based and traditional RS methods regarding cost, resolution, accuracy, and other factors. Integrating multiple sensors on a single platform and fusion of multi-source data enhance efficiency in geoscientific analysis. However, implementing geophysical methods on UAVs poses challenges, prompting ongoing research and development efforts worldwide to find optimal solutions from both hardware and software perspectives. Full article

The Jinshajiang fault zone is the western boundary fault of the Sichuan–Yunnan block, located east of the Qinghai–Tibetan Plateau. It is a complex tectonic suture belt with multi-phase activity and is characterized by multiple sets of parallel or intersecting faults. Using high-resolution image interpretation, seismic geological surveys, and trench studies, we examined the Holocene activity and obtained the paleoseismic sequences on the middle segment of the fault zone. Thus, we could analyze the kinematic characteristics of the fault and its potential risk of strong earthquakes. Our results indicated that the predominant movement of the fault zone was strike-slip motion. In the Jinshajiang fault zone, the Late Quaternary horizontal slip rates of the north-northeast-trending Yarigong fault and the northeast-trending Ciwu fault were 3.6 ± 0.6 mm/a and 2.5 ± 0.5 mm/a, respectively. Three paleoseismic events were identified on the Yarigong fault, dated 6745–3848, 3742–1899, and 1494–1112 cal BP, and on the Ciwu fault, constrained to 32,566–29,430, 24,056–22,990, and 2875–2723 cal BP. The last major earthquake on the Ciwu fault occurred approximately 2800 years ago; therefore, its future seismic hazard deserves attention. Full article

The availability of reliable and quantified information on the spatiotemporal distribution of irrigated land at the river basin scale is an essential step towards sustainable management of water resources. This research aims to assess the spatiotemporal extent of irrigated land in the Ouémé River basin using Landsat multi-temporal images and ground truth data. A methodology was built around the use of supervised classification and the application of an algorithm based on the logical expression and thresholding of a combination of surface temperature (Ts) and normalized difference vegetation index (NDVI). The findings of the supervised classification showed that agricultural areas were 16,003 km², 19,732 km², and 22,850 km² for the years 2014, 2018, and 2022, respectively. The irrigated land areas were 755 km², 1143 km², and 1883 km² for the same years, respectively. A significant increase in irrigated areas was recorded throughout the study period. The overall accuracy values of 79%, 82%, and 83% obtained during validation of the irrigated land maps indicate a good performance of the algorithm. The results suggest a promising application of the algorithm to obtain up-to-date information on the distribution of irrigated land in several regions of Africa. Full article

Remote sensing techniques provide crucial insights into ancient settlement patterns in various regions by uncovering previously unknown archaeological sites and clarifying the topological features of known ones. Meanwhile, in the northern part of the Southern Levant, megalithic structures remain largely underexplored with these methods. This study addresses this gap by analyzing the landscape around Rujm el-Hiri, one of the most prominent Southern Levantine megaliths dated to the Chalcolithic/Early Bronze Age, for the first time. We discuss the type and extent of the archaeological remains identified in satellite images within a broader context, focusing on the relationships between landscapes and these objects and the implications of their possible function. Our analysis of multi-year satellite imagery covering the 30 km region surrounding the Sea of Galilee reveals several distinct patterns: 40–90-m-wide circles and thick walls primarily constructed along streams, possibly as old as Rujm el-Hiri itself; later-period linear thin walls forming vast rectangular fields and flower-like clusters of ~ 20 m diameter round-shaped fences found in wet areas; tumuli, topologically linked to the linear walls and flower-like fences. Although tumuli share similar forms and likely construction techniques, their spatial distribution, connections to other archaeological features, and the statistical distribution in their sizes suggest that they might serve diverse functions. The objects and patterns identified may be used for further training neural networks to analyze their spatial properties and interrelationships. Most archaeological structures in the region were reused long after their original construction. This involved adding new features, building walls over older ones, and reshaping the landscape with new objects. Rujm el-Hiri is a prime example of such a complex sequence. Geomagnetic analysis shows that since the entire region has rotated over time, the Rujm el-Hiri’s location shifted from its original position for tens of meters for the thousands of years of the object’s existence, challenging theories of the alignment of its walls with astronomical bodies and raising questions regarding its possible identification as an observatory. Full article

Hierapolis of Phrygia, an archaeological site in southwestern Turkey, has been a UNESCO World Heritage Site since 1988. During archaeological campaigns, 71 mortar samples from public buildings were collected, dating from the Julio-Claudian to the Middle Byzantine period. The samples were analyzed using a multi-analytical approach including polarized optical microscopy (POM), digital image analysis (DIA), X-ray powder diffraction (XRPD) and SEM–EDS to trace the raw materials and understand the evolution of mortar composition and technology over time. During the Roman period, travertine and marble were commonly used in binder production, while marble dominated in the Byzantine period. The aggregates come mainly from sands of the Lycian Nappe and Menderes Massif, with carbonate and silicate rock fragments. Variations in composition, average size and circularity suggest changes in raw material sources in both Roman and Byzantine periods. Cocciopesto mortar was used in water-related structures from the Flavian to the Severan period, but, in the Byzantine period, it also appeared in non-hydraulic contexts. Straw became a common organic additive in Byzantine renders, marking a shift from the exclusively inorganic aggregates of Roman renders. This study illustrates the evolving construction technologies and material sources used throughout the city’s history. Full article

Neovascular age-related macular degeneration (nAMD) is one of the major causes of vision impairment that affect millions of people worldwide. Early detection of nAMD is crucial because, if untreated, it can lead to blindness. Software and algorithms that utilize artificial intelligence (AI) have become valuable tools for early detection, assisting doctors in diagnosing and facilitating differential diagnosis. AI is particularly important for remote or isolated communities, as it allows patients to endure tests and receive rapid initial diagnoses without the necessity of extensive travel and long wait times for medical consultations. Similarly, AI is notable also in big hubs because cutting-edge technologies and networking help and speed processes such as detection, diagnosis, and follow-up times. The automatic detection of retinal changes might be optimized by AI, allowing one to choose the most effective treatment for nAMD. The complex retinal tissue is well-suited for scanning and easily accessible by modern AI-assisted multi-imaging techniques. AI enables us to enhance patient management by effectively evaluating extensive data, facilitating timely diagnosis and long-term prognosis. Novel applications of AI to nAMD have focused on image analysis, specifically for the automated segmentation, extraction, and quantification of imaging-based features included within optical coherence tomography (OCT) pictures. To date, we cannot state that AI could accurately forecast the therapy that would be necessary for a single patient to achieve the best visual outcome. A small number of large datasets with high-quality OCT, lack of data about alternative treatment strategies, and absence of OCT standards are the challenges for the development of AI models for nAMD. Full article

This paper introduces WaterGPT, a language model designed for complex multimodal tasks in hydrology. WaterGPT is applied in three main areas: (1) processing and analyzing data such as images and text in water resources, (2) supporting intelligent decision-making for hydrological tasks, and (3) enabling interdisciplinary information integration and knowledge-based Q&A. The model has achieved promising results. One core aspect of WaterGPT involves the meticulous segmentation of training data for the supervised fine-tuning phase, sourced from real-world data and annotated with high quality using both manual methods and GPT-series model annotations. These data are carefully categorized into four types: knowledge-based, task-oriented, negative samples, and multi-turn dialogues. Additionally, another key component is the development of a multi-agent framework called Water_Agent, which enables WaterGPT to intelligently invoke various tools to solve complex tasks in the field of water resources. This framework handles multimodal data, including text and images, allowing for deep understanding and analysis of complex hydrological environments. Based on this framework, WaterGPT has achieved over a 90% success rate in tasks such as object detection and waterbody extraction. For the waterbody extraction task, using Dice and mIoU metrics, WaterGPT’s performance on high-resolution images from 2013 to 2022 has remained stable, with accuracy exceeding 90%. Moreover, we have constructed a high-quality water resources evaluation dataset, EvalWater, which covers 21 categories and approximately 10,000 questions. Using this dataset, WaterGPT achieved the highest accuracy to date in the field of water resources, reaching 83.09%, which is about 17.83 points higher than GPT-4. Full article

The ancient Roman town of Turris Libisonis was located on the northern coast of Sardinia and was known in the past as an important naval port. Located in the Gulf of Asinara, it was a Roman colony from the 1st century BCE and became one of the richest towns on the island. Among the archaeological finds in the area, the cippus exhibited in the Antiquarium Turritano is of great interest for its well-preserved traces of polychromy. The artefact dates back to the early Imperial Age and could have had a funerary or votive function. The artefact was first examined using a portable and non-invasive protocol involving multi-band imaging (MBI), portable X-ray fluorescence (p-XRF), portable FT-IR in external reflectance mode (ER FT-IR) and Raman spectroscopy. After this initial examination, a few microfragments were collected and investigated by optical microscopy (OM), X-ray powder diffraction (XRPD), Fourier-transform infrared spectroscopy in ATR mode (ATR FT-IR) and micro-ATR mode (μATR FT-IR) and Scanning Electron Microscopy/Energy Dispersive Spectroscopy (SEM-EDS) to improve our knowledge and characterize the materials and to determine their provenience. The results contribute to a better understanding of the provenance of materials and shed light on pigments on stone and their use outside the Italian peninsula and, in particular, Roman Sardinia. Full article

In this contribution, we consider an application of the Kirchhoff migration (KM) technique for fast and accurate identification of small dielectric objects from two-dimensional Fresnel experimental dataset. Generally, for successful application of the KM, a complete set of elements from the so-called multi-static response (MSR) matrix must be collected; however, in the Fresnel experimental dataset, many of the elements of an MSR matrix are not measurable. Nevertheless, the existence, location, and outline shape of small objects can be retrieved using the KM by converting unavailable data into the zero constant. However, the theoretical reason behind such conversion has not been confirmed to date. In order to explain this theoretical reason, we convert unavailable measurement data into a constant and demonstrate that the imaging function of the KM can be expressed by an infinite series of the Bessel functions of integer order of the first kind, the object’s material properties, and the converted constant. Following the theoretical result, we confirm that converting unknown data into the zero constant guarantees good results and unique determination of the objects. Finally, various numerical simulation results from Fresnel experimental dataset are presented and discussed to validate the theoretical result. Full article

SAR images with two polarizations show strong potential for mapping forest stock volume (FSV) combined with limited samples. However, accurately mapping FSV still presents challenges in selecting the optimal acquisition date to obtain the SAR images during specific phenological stages within the annual forest cycle (growth and dormant stages). To clarify the impacts of phenological stages within the annual cycle on FSV mapping, SAR images with various polarization models and bands (Sentinel-1(S), GaoFen-3(GF-3 (G)) and ALOS-2(A)) were acquired within the growth and dormant stages of an annual cycle in a boreal evergreen coniferous forest (Chinese pine) and a deciduous coniferous forest (Larch). Subsequently, single-band (G, S, and A) and multi-band combined alternative variable sets (A + G, A + S, S + G, and A + S + G) were extracted within the same stage, respectively. Finally, the forward selection approach was utilized in conjunction with four different models (MLR, KNN, RF, and SVR) to obtain the most suitable variable sets and generate FSV mapping. The results demonstrated a strong correlation between the intensity of backscattering coefficients and the phenological stages of the forest. Within the dormant stage, there was a significant decrease in the gaps of backscattering coefficients obtained from the same polarization compared to those within the growth stage. Furthermore, the results also revealed that more signals from inside the canopy could be detected during the dormant stage in both evergreen coniferous forests and deciduous coniferous forests. Subsequently, the accuracy in mapping FSV obtained from single-band SAR images within the dormant stage are slightly higher than that within the growth stage, and the accuracy was still significantly affected by both overestimation and underestimation. Moreover, the combined effects of different bands significantly improve the reliability of mapped FSV. The rRMSE values in four multi-band combinations ranged from 22.37% to 29.40% for Chinese pine forests and from 21.27% to 34.38% for Larch forests, and the optimal result was observed from combinations of A + S + G acquired within the dormant stage. It is confirmed that SAR signal and their sensitivity to FSV depends on the stages of forest annual growth cycle. In comparison to the growth period, dual-polarization SAR data acquired during the dormant stage is more suitable for estimating FSV in boreal forests. Full article

Growth monitoring of crops is a crucial aspect of precision agriculture, essential for optimal yield prediction and resource allocation. Traditional crop growth monitoring methods are labor-intensive and prone to errors. This study introduces an automated segmentation pipeline utilizing multi-date aerial images and ortho-mosaics to monitor the growth of cauliflower crops (Brassica Oleracea var. Botrytis) using an object-based image analysis approach. The methodology employs YOLOv8, a Grounding Detection Transformer with Improved Denoising Anchor Boxes (DINO), and the Segment Anything Model (SAM) for automatic annotation and segmentation. The YOLOv8 model was trained using aerial image datasets, which then facilitated the training of the Grounded Segment Anything Model framework. This approach generated automatic annotations and segmentation masks, classifying crop rows for temporal monitoring and growth estimation. The study’s findings utilized a multi-modal monitoring approach to highlight the efficiency of this automated system in providing accurate crop growth analysis, promoting informed decision-making in crop management and sustainable agricultural practices. The results indicate consistent and comparable growth patterns between aerial images and ortho-mosaics, with significant periods of rapid expansion and minor fluctuations over time. The results also indicated a correlation between the time and method of observation which paves a future possibility of integration of such techniques aimed at increasing the accuracy in crop growth monitoring based on automatically derived temporal crop row segmentation masks. Full article

Digital Surface Model (DSM) generation from high-resolution optical satellite images is an important topic of research in the remote sensing field. In optical satellite imaging systems, the attitude information of the cameras recorded by satellite sensors is often biased, which leads to errors in the Rational Polynomial Camera (RPC) model of satellite imaging. These errors in the RPC model can mislead the DSM generation. To solve the above problems, we propose an automatic DSM generation method from satellite images based on the Double-Penalty bundle adjustment (DPBA) optimization algorithm. In the proposed method, two penalty functions representing the camera’s attitude and the spatial 3D points, respectively, are added to the reprojection error model of the traditional bundle adjustment optimization algorithm. Instead of acting on images directly, the penalty functions are used to adjust the reprojection error model and improve the RPC parameters. We evaluate the performance of the proposed method using high-resolution satellite image pairs and multi-date satellite images. Through some experiments, we compare the accuracy and completeness of the DSM generated by the proposed method, the Satellite Stereo Pipeline (S2P) method, and the traditional bundle adjustment (BA) method. Compared to the S2P method, the experiment results of the satellite image pair indicate that the proposed method can significantly improve the accuracy and the completeness of the generated DSM by about 1–5 m and 20%–60% in most cases. Compared to the traditional BA method, the proposed method improves the accuracy and completeness of the generated DSM by about 0.01–0.05 m and 1%–3% in most cases. The experiment results can be a testament to the feasibility and effectiveness of the proposed method. Full article