Hotspot Detection Research Articles

Advancing Ki67 hotspot detection in breast cancer: acomparative analysis of automated digital image analysis algorithms.

Manual detection and scoring of Ki67 hotspots is difficult and prone to variability, limiting its clinical utility. Automated hotspot detection and scoring by digital image analysis (DIA) could improve the assessment of the Ki67 hotspot proliferation index (PI). This study compared the clinical performance of Ki67 hotspot detection and scoring DIA algorithms based on virtual dual staining (VDS) and deep learning (DL) with manual Ki67 hotspot PI assessment. Tissue sections of 135 consecutive invasive breast carcinomas were immunohistochemically stained for Ki67. Two DIA algorithms, based on VDS and DL, automatically determined the Ki67 hotspot PI. For manual assessment; two independent observers detected hotspots and calculated scores using a validated scoring protocol. Automated hotspot detection and assessment by VDS and DL could be performed in 73% and 100% of the cases, respectively. Automated hotspot detection by VDS and DL led to higher Ki67 hotspot PIs (mean 39.6% and 38.3%, respectively) compared to manual consensus Ki67 PIs (mean 28.8%). Comparing manual consensus Ki67 PIs with VDS Ki67 PIs revealed substantial correlation (r = 0.90), while manual consensus versus DL Ki67 PIs demonstrated high correlation (r = 0.95). Automated Ki67 hotspot detection and analysis correlated strongly with manual Ki67 assessment and provided higher PIs compared to manual assessment. The DL-based algorithm outperformed the VDS-based algorithm in clinical applicability, because it did not depend on virtual alignment of slides and correlated stronger with manual scores. Use of a DL-based algorithm may allow clearer Ki67 PI cutoff values, thereby improving the clinical usability of Ki67.

Markov chain quasi-Monte Carlo method for forecasting fire hotspots in Sarawak, Malaysia.

Stochastic modeling approaches have attracted many researchers to the field. However, fire hotspot detection suffers from not using a Markov chain quasi-Monte Carlo (MCQMC) as a forecasting methodology. This paper proposes improvements to the computational time by combining the strengths of the Markov chain Monte Carlo (MCMC) and quasi-Monte Carlo (QMC) methods. The proposed method can lead to more precise and stable results, particularly in problems with high-dimensional integration or complex probability distributions. The proposed method is applied to a case study of fire hotspot detection in Sarawak, Malaysia. The outcome of this study reveals that the MCQMC method is more computationally efficient, taking only 0.0746seconds compared to MCMC's 0.0914seconds and QMC's 0.0994seconds. It is shown that the best option derived by the proposed method is effective in predicting fire hotspots and providing quick solutions to protect the environment and communities from forest fires.

Investigation of an Optimal Sampling Resolution to Support Soil Management Decisions for Urban Plots.

The main objective of the current study was to use seven lots in Hartford, CT that are planned for community reuse to determine the optimal sampling density that allows for the detection of hotspots of lead pollution while limiting the labor of the sampling process. The sampling density was investigated using soil Pb measured by in situ X-ray Fluorescence as the indicator to evaluate soil health, with a new threshold of 200-mg/kg proposed by the USEPA in January of 2024. Even though this study takes place in an urban setting, where the new USEPA policy requires the use of a 100-mg/kg threshold for Pb due to the fact that there are other identifiable sources of the contaminant, only the 200-mg/kg threshold is discussed because it is evident from the analysis that compliance of a 100 mg/kg threshold in urban plots is highly unlikely (five out of seven sites would require complete site excavation prior to reuse). Using the inverse distance weighted geospatial interpolation of in situ pXRF determined lead measurements, grid sampling resolutions of 3-m, 4-m, 5-m, 6-m, 8-m, 10-m, and 12-m were compared. Ultimately, the case study finds that the largest grid resolution that can be implemented for soil screening to maintain hotspots of pollution to properly inform soil management decisions is a 6-m grid, or a density of approximately 1/36-m2.

Latitudinal Asymmetry in the Dayside Atmosphere of WASP-43b

We present two-dimensional near-infrared temperature maps of the canonical hot Jupiter WASP-43b using a phase-curve observation with JWST NIRSpec/G395H. From the white-light planetary transit, we improve constraints on the planet’s orbital parameters and measure a planet-to-star radius ratio of 0.15883−0.00053+0.00056 . Using the white-light phase curve, we measure a longitude of maximum brightness of 6.9−0.°5+0.°5 east of the substellar point and a phase-curve offset of 10.0−0.°8+0.°8 . We also find a ≈4σ detection of a latitudinal hotspot offset of −13.4−1.°7+3.°2 , the first significant detection of a nonequatorial hotspot in an exoplanet atmosphere. We show that this detection is robust to variations within planetary parameter uncertainties, but only if the transit is used to improve constraints, showing the importance of transit observations to eclipse mapping. Maps retrieved from the NRS1 and NRS2 detectors are similar, with hotspot locations consistent between the two detectors at the 1σ level. Our JWST data show brighter (hotter) nightsides and a dimmer (colder) dayside at the shorter wavelengths relative to fits to Spitzer 3.6 and 4.5 μm phase curves. Through comparison between our phase curves and a set of general circulation models, we find evidence for clouds on the nightside and atmospheric drag or high metallicity reducing the eastward hotspot offset.

Assessing network-based traffic crash risk using prospective space-time scan statistic method

As car ownership and urbanization continue to rise worldwide, traffic crashes have become growing concerns globally. Measuring crash risk provides insight into understanding crash patterns, which can eventually support proactive transport planning and improve road safety. However, traditional spatial analysis methods for crash risk assessment, such as the hotspot detection method, are mainly focused on identifying areas with higher crash frequency. These methods are subject to critical issues in risk analysis due to ignoring crash impacts and background traffic volume information. Aside from the two issues, current crash risk assessment methods, especially those aiming for cluster detection, are subject to the modified temporal unit problem, referring to the temporal effects (i.e., aggregation, segmentation, and boundary) in cluster detection. To alleviate these issues, this paper applies an emerging hot spot detection method, called the prospective space-time scan statistic (STSS) method, for assessing the crash risk at a refined network scale and over multiple years in a case study of Hartford, Connecticut. By identifying the spatial and temporal clusters of the crash risk, the study can provide evidence for tailoring road safety management strategies in neighborhoods characterized by high crash risk.

The establishment of a molecular diagnostic platform for mitochondrial diseases: from conventional to next-generation sequencing

BackgroundThe aim of this study was to create a molecular diagnostic platform and establish a diagnostic pipeline for patients highly suspected of mitochondrial disorders. The effectiveness of three methods, namely, traditional restriction fragment length polymorphism-polymerase chain reaction (RFLP-PCR), Sanger sequencing for hotspot detection and whole mitochondrial DNA (mtDNA), and third-generation (Nanopore) whole mtDNA sequencing, will be compared in diagnosing patients with suspected primary mitochondrial diseases (PMDs). The strengths and limitations of different methods are also discussed. Material and methodsA single-center prospective cohort study was conducted to validate the diagnostic pipeline for suspected mitochondrial diseases. In the first stage, a PCR-based method with five sets of primers was used to screen for eight hotspots (m.3243A>G, m.3460G>A, m.8344A>G, m.8993T>G, m.9185T>C, m.11778G>A, m.13513G>A, and m.4977deletion) using either RFLP or direct Sanger sequencing. Sanger sequencing was also used to confirm the RFLP-positive samples. In the second stage, for samples with negative screening results for the eight hotspots, mitochondrial whole-genome sequencing was performed using Sanger sequencing or third-generation nanopore sequencing. ResultsBetween June 2020 and May 2023, 30 patients from ages 0 to 63 with clinically suspected mitochondrial disease were enrolled. The positive yield for the diagnosis of PMDs was 8/30=26.7%, and the sensitivity of the heteroplasmy level for the RFLP-based method was approximately 5%. The remaining 22 patients who tested negative at the first stage were tested using Sanger sequencing or the third-generation sequencing Nanopore, and all tested negative for pathological mtDNA mutations. Compared to the Sanger sequencing method, the results of RFLP-PCR were compromised by the limitations of incomplete RFLP enzyme digestion. For whole-genome sequencing of mtDNA, Sanger sequencing, instead of nanopore sequencing, is preferred at our institution because of its cost-effectiveness. Conclusions: In our highly selective cohort, most tested positive in the first stage of the 8 hot spots screen. Sanger sequencing is a conventional and accurate method for mitochondrial disease screening, at least for the most common hot spots in the region. The results revealed that Sanger sequencing is an accurate method with the benefit of being more cost-effective. This integral platform of molecular diagnosis bears the advantages of being relatively low cost and having a shorter reporting time, facilitating crucial identification of patients with clinical evidence of such disorders. This diagnostic flowchart has also been translated into routine clinical use in the tertiary hospital.

Graph-Based Hotspot Detection of Socio-Economic Data Using Rough-Set

The term hotspot refers to a location or an area where the occurrence of a particular phenomenon, event, or activity is significantly higher than in the surrounding areas. The existing statistical methods need help working well on discrete data. Also, it can identify a false hotspot. This paper proposes a novel graph-based hotspot detection using a rough set (GBHSDRS) for detecting the hotspots. This algorithm works well with discrete spatial vector data. Furthermore, it removes the false hotspot by finding the statistical significance of the identified hotspots. A rough set theory is applied to the graph of the spatial polygon data, and the nodes are divided into lower, boundary, and negative regions. Therefore, the candidate hotspot belongs to the lower region of the set, and the boundary value analysis will ensure the identification of the hotspots if the hotspot is present in the dataset. The p-value is used to find the statistical significance of the hotspots. The algorithm is tested on the socioeconomic data of Uttar Pradesh (UP) from 1991 on medical facilities. The average gain in density and Hotspot Prediction Accuracy Index (HAPI) of the detected hotspots is 26.54% and 23.41%, respectively. An average reduction in runtime is 27.73%, acquired compared to all other methods on the socioeconomic data.

Deep learning model for detection of hotspots using infrared thermographic images of electrical installations

Hotspots in electrical power equipment or installations are a major issue whenever it occurs within the power system. Factors responsible for this phenomenon are many, sometimes inter-related and other times they are isolated. Electrical hotspots caused by poor connections are common. Deep learning models have become popular for diagnosing anomalies in physical and biological systems, by the instrumentality of feature extraction of images in convolutional neural networks. In this work, a VGG-16 deep neural network model is applied for identifying electrical hotspots by means of transfer learning. This model was achieved by first augmenting the acquired infrared thermographic images, using the pre-trained ImageNet weights of the VGG-16 algorithm with additional global average pooling in place of conventional fully connected layers and a softmax layer at the output. With the categorical cross-entropy loss function, the model was implemented using the Adam optimizer at learning rate of 0.0001 as well as some variants of the Adam optimization algorithm. On evaluation, with a test IRT image dataset, and a comparison with similar works, the research showed that a better accuracy of 99.98% in identification of electrical hotspots was achieved. The model shows good score in performance metrics like accuracy, precision, recall, and F1-score. The obtained results proved the potential of deep learning using computer vision parameters for infrared thermographic identification of electrical hotspots in power system installations. Also, there is need for careful selection of the IR sensor’s thermal range during image acquisition, and suitable choice of color palette would make for easy hotspot isolation, reduce the pixel to pixel temperature differential across any of the images, and easily highlight the critical region of interest with high pixel values. However, it makes edge detection difficult for human visual perception which computer vision-based deep learning model could overcome.

Bioinformatic Identification of TP53 Gene Mutation Hotspots in Colorectal Cancer

Mutations and inactivation of the TP53 gene are frequently observed in various types of malignancies. Precise knowledge of the genetic structure and detection of mutation hotspots are crucial, as these indicate a high probability of developing cancer. The aim of our study was to perform the bioinformatic detection of mutation hotspots in the TP53 gene in patients diagnosed with malignant colon neoplasms using self-developed software (version 1). We compared TP53 gene sequences from 50 healthy individuals with those from 50 patients diagnosed with colorectal carcinoma. Of the 50 samples from cancer patients, the most frequent mutations were observed in exons 5 and 8 (12 mutations per exon) and gene sequences of 12 samples, which differed from those of the 50 samples from healthy individuals. Based on our results, the distribution of mutations in the TP53 gene structure was not even across different exons. By comparing the gene sequences of healthy individuals with those of colon cancer samples, we conclude that structural changes occurring in similar gene regions are not associated with increases in susceptibility to malignancies in every case, namely, that the pathological mechanism is multifactorial.

Moss Bags as Active Biomonitors of Air Pollution: Current State of Understanding, Applications and Concerns

Dual concerns involving the rise in airborne pollutant levels and bulging need to protect-preserve human health have propelled the search for innovative means for air quality monitoring to aid in evidence-based decision-making (pollution prevention-mitigation). In this regard, moss bags have gathered a great deal of attention as active biomonitors. In this reflective discourse, we systematically review the world literature to present a bird’s eye view of moss bag applications and advances while highlighting potential concerns. We begin with a brief note on mosses as biomonitors, highlighting the advantages of moss bags over the passive technique (native moss), other living organisms (lichens, vascular plants), and instrument-based measurements. A major strand of moss bag research involves urban ecosystem sustainability studies (e.g., street tunnels and canyons, parks), while others include event-specific monitoring and change detection (e.g., SARS-CoV-2 Lockdown), indoor-outdoor air quality assessment, and change detection in land use patterns. Recent advances include biomagnetic studies, radioisotopic investigations, and mobile applications. Efforts are currently underway to couple moss bag results with a suite of indicators [e.g., relative accumulation factor (RAF), contamination factor (CF), pollution load index (PLI), enrichment factor (EF)] and spatially map the results for holistic appraisal of environmental quality (hot spot detection). However, while moss bag innovations and applications continue to grow over time, we point to fundamental concerns/uncertainties (e.g., lack of concordance in operational procedures and parameterization, ideal species selection, moss vitality) that still need to be addressed by targeted case studies, before the moss results could be considered in regulatory interventions.

Estimating built-up area carbon emissions through addressing regional development disparities with population and nighttime light data

Carbon emission inventories in urban built-up areas are critical for formulating effective emission reduction policies. Nighttime light data, reflecting socioeconomic activities, is routinely used as a proxy for estimating emissions. However, the reliability of this proxy is limited in areas with minimal or absent lighting and demonstrates variability according to the urban development. To address these limitations, a novel approach was developed. Firstly, it enhances the representation of human activities under different light values through integrating light values and population distributions into multidimensional proxy features. Secondly, cities with different socio-economic development were clustered to minimize the development disparities within the same cluster. Four clusters were detected including ‘Agriculture-dependent & Low-density’, ‘Agriculture-dependent & High-density’, ‘Industry-oriented’, and ‘Tertiary-oriented’. Then A multilayer perceptron regressor, refined with a moving average method, was employed to model the nonlinear relationships between emissions and these proxy features. The results show a significant increase in modeling accuracy when regional development disparities are incorporated through this clustering strategy. Additionally, the Shapley Additive Explanatory method was utilized to analyze the influence of regional development heterogeneity on the estimation model. It demonstrated that areas with high light intensity and population significantly influence emission estimates in all clusters, except for the ‘Industry-oriented cluster’. This finding underscores the varying effectiveness of proxy features across different regions, emphasizing the importance of considering regional development disparities. Finally, based on the estimated inventory obtained, spatio-temporal hot spot detection pinpointed eastern and central China as significant emission hot spots. This research not only enhances the data resources for managing carbon emissions but also underscores the need for inter-city cooperation strategies to achieve China's carbon reduction targets.

Spatial patterns of intestinal parasite infections among children and adolescents in some indigenous communities in Argentina.

Argentina has a heterogeneous prevalence of infections by intestinal parasites (IPs), with the north in the endemic area, especially for soil-transmitted helminths (STHs). We analyzed the spatial patterns of these infections in the city of Tartagal, Salta province, by an observational, correlational, and cross-sectional study in children and adolescents aged 1 to 15 years from native communities. One fecal sample per individual was collected to detect IPs using various diagnostic techniques: Telemann sedimentation, Baermann culture, and Kato-Katz. Moran's global and local indices were applied together with SaTScan to assess the spatial distribution, with a focus on cluster detection. The extreme gradient boosting (XGBoost) machine-learning model was used to predict the presence of IPs and their transmission pathways. Based on the analysis of 572 fecal samples, a prevalence of 78.3% was found. The most frequent parasite was Giardia lamblia (30.9%). High- and low-risk clusters were observed for most species, distributed in an east-west direction and polarized in two large foci, one near the city of Tartagal and the other in the km 6 community. Spatial XGBoost models were obtained based on distances with a minimum median accuracy of 0.69. Different spatial patterns reflecting the mechanisms of transmission were noted. The distribution of the majority of the parasites studied was aligned in a westerly direction close to the city, but the STH presence was higher in the km 6 community, toward the east. The purely spatial analysis provides a different and complementary overview for the detection of vulnerable hotspots and strategic intervention. Machine-learning models based on spatial variables explain a large percentage of the variability of the IPs.

COVID-19 hotspot detection in a university setting.

The onset of the COVID-19 pandemic commenced an era of widespread disruptions in the academic world, including shut downs, periodic shifts to online learning, and disengagement from students. In an effort to transition back to in-person learning, many universities and schools tried to implement policy that balanced student learning with community health. While academic administrators have little control over some aspects of COVID-19 spread, they often choose to use temporary shutdowns of in-person teaching based on perceived hotspots of COVID-19. Specifically, if administrators have substantial evidence of within-group transmission for a class or other academic unit (a "hotspot"), the activities of that class or division of the university might be temporarily moved online. In this article, we describe an approach used to make these types of decisions. Using demographic information and weekly COVID-19 testing outcomes for university students, we use an XGBoost model that produces an estimated probability of testing positive for each student. We discuss variables engineered from the demographic information that increased model fit. As part of our approach, we simulate semesters under the null hypothesis of no in-class transmission, and compare the distribution of simulated outcomes to the observed group positivity rates to find an initial p-value for each group (e.g., section, housing area, or major). Using a simulation-based modification of a standard false discovery rate procedure, we identify possible hot spots-classes or groups whose COVID-19 rates exceed the levels expected for the demographic mix of students in each group of interest. We use simulation experiments and an anonymized example from Fall 2020 to illustrate the performance of our approach. While our example is based on hotspot detection in a university setting, the approach can be used for monitoring the spread of infectious disease within any interconnected organization or population.

Drone Swarm for Post-Wildfire Hot Spot Detection: Technology Assessment and PoC Demonstrator

Post-wildfire hot spots are a severe problem for fire fighters since they are hard to detect and can reignite fires. The tedious search can be greatly improved by using drones. However, developing and controlling an autonomous drone swarm is a challenging task. An assessment with domain experts reveals the suitability of drone swarms for hot spot detection. We develop a heterogeneous multi-drone system that can support fire fighters by creating thermal and visual aerial images and automating the hot spot detection process. During a joint exercise with a fire fighter brigade we demonstrate that the proposed system is able to autonomously search, detect, and report hot spots. Therefore it is able to speed up the search process while reducing the required number of personnel.

Convolutional neural networks and Internet of Things for fault detection by aerial monitoring of photovoltaic solar plants

Aerial thermographic inspection is performed with thermal cameras embedded in unmanned aerial vehicles, being one of the most relevant monitoring techniques for photovoltaic panels. This technique allows the detection of thermal patterns associated with faults in the photovoltaic panels, although image analysis and fault detection require novel processing methodologies. The volume and variety of aerial thermal data together with the need for faster and more efficient analysis for early maintenance operations require efficient Internet of Things architectures to provide fast and effective diagnostics with easy access.This paper presents a new approach based on image analysis with two consecutive convolutional neural networks to detect hot spots in an Internet of Things platform and reduce the number of false positives. The architecture of the platform is designed to automatically process the received data through the implementation of different Convolutional Neural Networks. A real case study is proposed with thermograms from three photovoltaic solar plants with different sizes, shapes of panels and a wide temperature range. The results show an accuracy of 99 % for panel detection and 96 % for hot spot detection with a reduction of false positives compared to other studies, such as Support Vector Machine or different Artificial Neural Networks, demonstrating the robustness of the method.

A method for detecting photovoltaic panel faults using a drone equipped with a multispectral camera

Abstract. Photovoltaic power stations utilizing solar energy, have grown in scale, resulting in an increase in operational maintenance requirements. Efficient inspection of components within these stations is crucial. However, the large area of photovoltaic power generation, coupled with a substantial number of photovoltaic panels and complex geographical environments, renders manual inspection methods highly inefficient and inadequate for modern photovoltaic power stations. To address this issue, this paper proposes a method and system for hot spot detection on photovoltaic panels using unmanned aerial vehicles (UAVs) equipped with multispectral cameras. The UAVs capture visible and infrared images of the photovoltaic power plant, which are then processed for photogrammetry to determine imaging position and attitude. The infrared images are stitched together using this information, forming a geographically referenced overall image. Hot spot detection is performed on the infrared images, enabling the identification of faulty photovoltaic panels and facilitating efficient inspection and maintenance. Experimental trials were conducted at a photovoltaic power station in Qingyuan, Guangdong Province China. The results demonstrate the effectiveness of the proposed method in accurately detecting panels with hot spot faults.

Lithography Hotspot Detection Based on Heterogeneous Federated Learning With Local Adaptation and Feature Selection

Lithography Hotspot Detection Based on Heterogeneous Federated Learning With Local Adaptation and Feature Selection

Design of the sparrow search algorithm (SSA) for airborne radioactive hotspot detection

In the context of using aircraft as a pivotal tool for detecting radioactive hotspots, the acquisition of radioactivity data was conducted through a CeBr3 scintillation crystal detector mounted on a helicopter. However, challenges arose, including managing extensive data volumes, computationally demanding tasks, and susceptibility to local optima issues. To address these challenges and leverage the benefits of the Sparrow Search Algorithm (SSA) in global optimization and convergence speed, an improved SSA was devised. This improved version integrated SSA principles with the intricacies of searching for radioactive hotspots. The algorithm employed a matrix segmentation method to process data matrices derived from measured data, aiming to enhance efficiency and accuracy. An empirical analysis was conducted, performing 100 iterations on an experimental matrix to scrutinize the impact of matrix segmentation. Computation times and results were compared across different segmentation levels, confirming the favorable algorithmic outcomes of the method. The practical viability and convergence stability of the algorithm were further assessed using genuine measured data, with segmented matrices generated for evaluation. Remarkably, a comparison between computational outcomes and manually identified data reaffirmed the algorithm's reliability in effectively detecting radioactive hotspots.

Automatic identification and quantification of volcanic hotspots in Alaska using HotLINK: the hotspot learning and identification network

An increase in volcanic thermal emissions can indicate subsurface and surface processes that precede, or coincide with, volcanic eruptions. Space-borne infrared sensors can detect hotspots—defined here as localized volcanic thermal emissions—in near-real-time. However, automatic hotspot detection systems are needed to efficiently analyze the large quantities of data produced. While hotspots have been automatically detected for over 20 years with simple thresholding algorithms, new computer vision technologies, such as convolutional neural networks (CNNs), can enable improved detection capabilities. Here we introduce HotLINK: the Hotspot Learning and Identification Network, a CNN trained to detect hotspots with a dataset of −3,800 satellite-based, Visible Infrared Imaging Radiometer Suite (VIIRS) images from Mount Veniaminof and Mount Cleveland volcanoes, Alaska. We find that our model achieves an accuracy of 96% (F1-score 0.92) when evaluated on −1,700 unseen images from the same volcanoes, and 95% (F1-score 0.67) when evaluated on −3,000 images from six additional Alaska volcanoes (Augustine Volcano, Bogoslof Island, Okmok Caldera, Pavlof Volcano, Redoubt Volcano, Shishaldin Volcano). In comparison with an existing threshold-based hotspot detection algorithm, MIROVA (Coppola et al., Geological Society, London, Special Publications, 2016, 426, 181–205), our model detects 22% more hotspots and produces 12% fewer false positives. Additional testing on −700 labeled Moderate Resolution Imaging Spectroradiometer (MODIS) images from Mount Veniaminof demonstrates that our model is applicable to this sensor’s data as well, achieving an accuracy of 98% (F1-score 0.95). We apply HotLINK to 10 years of VIIRS data and 22 years of MODIS data for the eight aforementioned Alaska volcanoes and calculate the radiative power of detected hotspots. From these time series we find that HotLINK accurately characterizes background and eruptive periods, similar to MIROVA, but also detects more subtle warming signals, potentially related to volcanic unrest. We identify three advantages to our model over its predecessors: 1) the ability to detect more subtle volcanic hotspots and produce fewer false positives, especially in daytime images; 2) probabilistic predictions provide a measure of detection confidence; and 3) its transferability, i.e., the successful application to multiple sensors and multiple volcanoes without the need for threshold tuning, suggesting the potential for global application.

Design and application of coastal erosion indicators using satellite and drone data for a regional monitoring program

Coastal erosion is a complex issue that affects landforms at various spatio-temporal scales, with concerns rising due to the impending threats of climate change. Given the increasing availability of coastal shoreline data, we identify a gap in present coastal management methods: the need for a simple and cost-effective mechanism to track and compare shoreline trends, to inform high-level reporting, down to local management scales. This study introduces simplified, automated erosion indicators, demonstrated for 20 morphologically diverse beach sites across the temperate coast of Victoria, Australia. The methods utilise coastal drone surveys (2018–2023; 560 surveys) and satellite-derived shorelines (Digital Earth Australia Coastlines, 1988 to 2021). Two tools are introduced: (1) the Erosion Warning Indicator, for high-level reporting, reducing multiple aspects of shoreline change (long-term vs. short-term; widespread vs. hotspot) to a simplified scoring system allowing overall site assessment and comparison across sites; and (2) the Erosion Hotspot Detector, to aid local managers to automatically identify and analyse emerging erosion areas. Results for a 2-year period (2021-22) indicate one site with a high erosion warning level, and four sites with a moderate level. Tracking the indicator over time (2018–2022) identifies two sites with persistently high erosion warning levels (Apollo Bay and Marengo). Importantly, the indicators also highlight areas where recent erosion trends differ from longer-term patterns (e.g., Inverloch), allowing re-evaluation of short-term expenditure on management interventions, without impacting longer-term adaptation pathways. The erosion indicators presented here hold value for coastal managers in many parts of the world. If comparable approaches are introduced and shared at scale, we will collectively be better equipped and informed to respond to future challenges to our coastlines.