Large image datasets have driven innovations in image-based machine learning, especially supervised object detection. However, manual annotation of such datasets has been challenging due to the issues related to labeling time, the subjectivity of human perception, and the scalability of dataset size. To that end, semi-supervised object detection (SSOD) methods have emerged that leverage a small amount of manually labeled and much larger unlabeled data. However, existing SSOD methods have been trained over clean, noise-free datasets such as MS-COCO and PASCAL VOC. They thus may fail to capture the reality, i.e., the heterogeneity and nuances of objects in the real world, which contain noises such as occlusion, illumination, and irregularity in the object’s shape. This study conducts an empirical analysis of the performance of semi-supervised object detection on a real-world custom dataset of homeless encampments. Results show that certain noise conditions may be ignored during the manual annotation process without causing a significant drop-off in SSOD performance across various metrics.

Effective monitoring of essential road infrastructure is critical to ensure economic growth and community connectivity, as deterioration from climate change and intensive use can compromise safety and hamper emergency services. To address these critical issues, we propose optimized approaches for road damage detection using YOLOv5, YOLOv8, and YOLOv10 models, with fine-tuning and hyperparameter optimization to improve detection accuracy. Our approaches achieve good overall accuracy (in terms of F1-score) by combining models of different sizes and frameworks using ensemble methods. Moreover, to enhance the speed of inference, parallel processing techniques are used. In order to obtain fast detection performance, small models, such as YOLOv5-small and YOLOv8-nano were given priority in modeling to balance between detection accuracy and inference speed. In the context of the 2024 IEEE BigData Optimized Road Damage Detection Challenge (ORDDC’2024), our approach achieved an F1 score greater than 0.7 with an inference speed of 0.0432 seconds per image, meeting the ORDDC’s accuracy and inference speed goal.

Monitoring public facilities is essential for governments to establish safer, cleaner, and more resilient cities, ultimately benefiting all citizens. One promising approach for public facility surveillance is to employ visual machine-learning methods on street scenes. The potential development and advancements in such state-of-the-art methods require the availability of visual datasets annotated with several public facility defects (e.g., illegal dumping, graffiti, street potholes, and damaged traffic signs). Towards this, we introduce "StreetLens", a new dataset of videos capturing urban streets with plentiful annotations for vision-based public facility monitoring. This dataset includes four-and-a-half hours of videos recorded by smartphone cameras placed in moving vehicles in the suburbs of three different cities.

Wide area urban street monitoring is highly demanding in various smart city applications. Manual monitoring is both laborious and time-consuming, hence automatic vision-based monitoring is a more feasible alternative. An essential part of vision-based street monitoring is detecting and counting objects of interest. However, these tasks are not straightforward due to various challenges, i.e., noisy conditions in a real environment, such as occlusion and high illumination. This study investigates the impact of these challenges on object detection and counting accuracy, then provides an empirical study to address the challenges with respect to video-based street monitoring. The selected case study demonstrates detecting and counting of homeless encampments in Los Angeles streets using street-level videos collected from a moving vehicle.

The analysis of trajectory datasets has numerous applications ranging from urban planning to human mobility understanding, but to protect the privacy of individuals trajectory datasets are rarely released to researchers. And even when they are, they are limited in size and spatio-temporal coverage. To address these issues a number of methods for generating synthetic yet realistic trajectory datasets have been proposed. These existing methods either require a lot of complex parameters to be calibrated (simulators) or rely on existing trajectory datasets (generative models). In this paper, we propose Data-Driven Trajectory Generator, dubbed DDTG, a data-driven, model-free, and parameter-less algorithm for generating realistic synthetic vehicle trajectory datasets. Unlike existing approaches, DDTG relies on aggregate origin-destination and traffic data, both of which are publicly available and free of privacy concerns. Furthermore, we show that our method is orthogonal to the existing approaches with which DDTG can be combined to generate synthetic datasets of higher quality. Our experiments with real-world trajectory and traffic data show that the datasets generated by DDTG follow distributions that are very close to the distributions of real trajectory datasets.

Automated monitoring systems have been utilized for effective road maintenance in order to eliminate time-consuming and manual inspection by road administration employees. Image-based technology has grown as an important option since a variety of images, such as from surveillance cameras or on-dash cameras, are widely obtainable. Towards that solution, this paper introduces a deep learning-based approach for detecting and classifying road damages in diverse images collected from different countries. Our approach integrates individual models trained per country and a general model trained for all countries. Our approach were evaluated thoroughly using the 2022 IEEE BigData Crowdsensing-based Road Damage Detection Challenge (CRDDC) Datasets. Experimental results show that our approach achieved an F1 score of up to 0.73.

The pervasive deployment of IoT devices along with the advancements in Deep Neural Network (DNN) models have enabled video analytics at the edge, the so-called Edge AI systems, in support of various large smart-city applications such as automatic road damage evaluation and fire detection. Current solutions require the model developer to make the placement decision by manually assigning models to edge devices. However, an Edge AI solution could entail hundreds of mobile edge devices operating in a large geographical region (e.g., installed on vehicles) with various resource capabilities and different DNN models, hence rendering manual placement ineffective. This paper presents alternative methods to automatically place various models on a diverse set of edge devices, considering the geospatial coverage of video data, resource capabilities of edge devices, and the characteristics of the trained models. First, we mathematically formulate the model placement as an optimization problem which is proven to be NP-Hard. We then propose several heuristics to solve it efficiently and evaluate them with a real-world dataset collected along the 165 bus route trajectories in the City of San Francisco. Our placement algorithm yields a higher recall in object detection and is more robust to the uncertainty of the underlying location context, without sacrificing much utilization cost.

With the availability of massive amounts of visual data covering wide geographical regions, various image learning applications have emerged, including classifying the street cleanliness level, detecting forest fires or road hazards. Such applications share similar characteristics as they need to 1) detect specific objects or events (what), 2) associate the detected object with a location (where), and 3) know the time that the event happened (when). Advancements in image-based machine learning (ML) benefit these applications as they can automate the detection of objects of interest. Along with the edge computing (EC) paradigm, the processing cost is offloaded to the devices, hence reducing latency and communication cost. Moreover, sensors on the edge devices (e.g., GPS) enrich the collected data with metadata. However, a shortcoming of existing approaches is that they rely on pre-trained "static" models. Nonetheless, crowdsourced data at diverse locations can be leveraged to iteratively improve the robustness of a model. We refer to the aforementioned strategy as "spatial crowd-based learning".To showcase this class of applications, we present FloraVision, an end-to-end system that integrates ML, crowdsourcing, and EC to automate the detection, mapping, and exploration of California Native Plants. FloraVision implements a pipeline to collect and clean publicly available image data, train a lightweight MobileNet-based classification model, and then deploy the model on mobile devices. It leverages spatial crowd-based learning to iteratively evolve the initial model from crowdsourced data. Its mobile application facilitates detecting plants and mapping their geolocations. Finally, it allows end-users to submit ad hoc spatio-temporal nearest neighbor queries and visualizes the results in an augmented reality user interface. Although our application focuses on plants, several other applications follow similar architectural patterns.

Generating hyper-local news at scale is challenging because publicly available data is not provided at the desired spatial and temporal granularity. Besides, there is a lack of automated analytical and publishing tools. Crosstown Foundry, which is being actively developed and used by engineers and journalists, is a novel data-driven system that leverages a massive multi-modal dataset to generate personalized newsletters for Los Angeles County readers.

For efficient road maintenance, an automated monitoring system is required to avoid laboriously and time-consuming manual inspection by road administration crews. One potential solution is to utilize image processing-based technologies, especially, as various sources of images have readily been available, e.g., surveillance cameras, in-vehicle cameras, or smartphones. Such image-based solutions enable detecting and classifying road damages. This paper introduces deep learning-based image analysis for road damage detection and classification. Our ensemble learning approaches with test time augmentation were thoroughly evaluated using the 2020 IEEE Big Data Global Road Damage Detection Challenge Dataset. Experimental results show that our approaches achieved an F1 score of up to 0.67, allowing us to win the Challenge.

Due to the prevalence of GPS-equipped cameras (e.g., smartphones and surveillance cameras), massive amounts of geo-tagged images capturing urban streets are increasingly being collected. Consequently, many smart city applications have emerged, relying on efficient image search. Such searches include spatial-visual queries in which spatial and visual properties are used in tandem to retrieve similar images to a given query image within a given geographical region. Towards this end, new index structures that organize images based on both spatial and visual properties are needed to efficiently execute such queries. Based on our observation that street images are typically similar in the same spatial locality, index structures for spatial-visual queries can be effectively built on a spatial index (i.e., R*-tree). Therefore, we propose a class of R*-tree indexes, particularly, by associating each node with two separate minimum bounding rectangles (MBR), one for spatial and the other for (dimension-reduced) visual properties of their contained images, and adapting the R*-tree optimization criteria to both property types.

Advances in federated learning and edge computing advocate for deep learning models to run at edge devices for video analysis. However, the captured video frame rate is too high to be processed at the edge in real-time with a typical model such as CNN. Any approach to consecutively feed frames to the model compromises both the quality (by missing important frames) and the efficiency (by processing redundantly similar frames) of analysis. Focusing on outdoor urban videos, we utilize the spatial metadata of frames to select an optimal subset of frames that maximizes the coverage area of the footage. The spatial keyframe extraction is formulated as an optimization problem, with the number of selected frames as the restriction and the maximized coverage as the objective. We prove this problem is NP-hard and devise various heuristics to solve it efficiently. Our approach is shown to yield much better hit-ratio than conventional ones.

Recognizing materials using image analysis is a classic problem. However, little research has been done with the images which have visual impediments such as noise, obstacle, or painting. This paper introduces the problem of recognizing covered materials which are distorted visually (e.g., materials covered by graffiti). We propose a set of approaches to solve this problem using a class of deep learning and transfer learning models, and evaluate our approaches empirically using a large-scale real world dataset that displays street scenes containing various materials which are covered with graffiti. Our experiments show that recognizing covered materials using the state-of-the-art approach for material recognition produced an mAP of 19%, while our proposed approach achieved an mAP of 60%. This evidently demonstrates that an approach for plain material recognition is not suitable for recognizing covered materials; hence this problem should be treated differently as in our proposed approaches.

An isochrone is generally defined as a curve drawn on a map connecting points at which moving objects (e.g., cars) arrive at the same time. Their construction is an important task in many application domains. As an example, in urban planning, isochrones are essential when assessing the placement of public services like hospitals and fire departments. In this study, we formally define the isochrone and reverse isochrone problems, describe our approach to solving them and provide a fully functional system that is capable of visualizing the reachability in various ways. Unlike other studies, our approach is purely data-driven and does not depend on the underlying road network for computing the isochrone. Instead, we focus on directly processing trajectory data. Our system processes two real-world taxi datasets to visualize the reachability of the cities of Seoul and Xi'an. As our experiments show, our approach outperforms the traditional graph-theory techniques while eliminating the expensive need of preprocessing the data.

Due to the ubiquity of GPS-equipped cameras such as smartphones, more photos are getting automatically tagged with camera locations (referred to as geo-tagged images) so large-scale geo-tagged image datasets are available on the Web. And a significant portion of online images such as travel and surveillance may not be meaningful without their location information. Thus, image localization for untagged images has been studied. However, the point camera location of an image might be quite different from the location of the scene depicted in the image (referred to as scene location) rendering image localization inaccurate. To address this problem, we propose a data-centric framework for image scene localization using a CNN-based classification in three steps. First, the framework provides two mechanisms for constructing a reference image dataset tagged with scene locations. Second, a spatial-visual classification approach organizes a dataset spatially using R-tree to generate a set of geographical regions tightly bounding the image scene locations. Then, we train a classifier based on the classes of images corresponding to the generated regions. Finally, to enhance the classification accuracy, we train a set of hierarchical classification models utilizing the spatial hierarchical structure of the R-tree where the trained models enable learning the visual features of images at different geographical granularities. We evaluate our framework using a geo-tagged image dataset obtained from Google Street View and demonstrate that the utilization of scene locations enables localizing images far more accurately as compared with camera location based localization.

With recent advances in technology, it is feasible to conveniently monitor urban roads using various cameras, such as surveillance cameras, in-vehicle cameras, or smartphones, and recognize their conditions by detecting specific types of road damages in order to plan maintenance resources efficiently based on the identified spots. This paper describes a road damage type detection and classification solution submitted to the IEEE BigData Cup Challenge 2018. Our solution is based on the state-of-the-art deep learning methods for an object detection task. In particular, our approach utilizes an object detection algorithm to detect various types of road damages by training the detector on different image examples categorized into a set of damages defined by Japan Road Association. We evaluated our approach thoroughly using different versions of trained models. Our experiments show that our approach was able to achieve an F1 score up to 0.62.

No abstract available.

Sentiment analysis of disaster-related posts in social media can contribute to the situation awareness and better understanding of the dynamics of disaster events by identifying the polarity of sentiments expressed by the public. However, Even though many sentiment analysis techniques have been developed and available, there are still limitations in reliably using sentiment analysis since there is no dominantly accepted technique in disasters. Taking advantage of existing state-of-the-art sentiment classifiers, this paper proposes a novel framework for geo-spatial sentiment analysis of disaster-related social media data objects. Our framework addresses three types of challenges: the inaccuracy and discrepancy associated with various text and image sentiment classifiers, the geo-sentiment discrepancy among data objects in a local geographical area, and observing diverse sentiments from multimedia data objects (i.e., text and image). The extracted sentiments are aggregated geographically for the purpose of extracting more accurate local regional insights. For the evaluation of the framework, we explored Twitter and Flickr datasets at the time of Hurricane Sandy and Napa Earthquake and showed how our approach can provide a better understanding of disaster events.

With applications ranging from basic trajectory calculations to complex autonomous vehicle operations, detailed vehicle movement analysis has been getting more attention in academia and industry. So far, real-data driven analysis, e.g., utilizing advanced machine-learning, has used data from sensors such as GPS and accelerometer. However, such research requires quality datasets to enable accurate analysis. To that end, we have collected real vehicle movement data, Multimedia Sensor Data, that contain synchronized sensor data in fine granularity such as GPS, accelerometer, digital compass, gyroscope, and, most importantly, matching real video images recorded at driving time. These real video images provide a way to accurately label the sensor data in generating a quality dataset, e.g., a training dataset. Then, we performed preprocessing steps to clean and refine the raw data, subsequently converted the results into csv files, which are compatible with a wide variety of analysis tools. We also provided sample cases to demonstrate methods of identifying abnormal driving patterns such as moving over a speed bump. This dataset will be useful for researchers refining their analyses of vehicle movements.

When analyzing and processing videos, it has become increasingly important in many applications to also consider contextual information, in addition to the content. With the ubiquity of sensor-rich smartphones, acquiring a continuous stream of geo-spatial metadata that includes the location and orientation of a camera together with the video frames has become practical. However, no such detailed dataset is publicly available. In this paper we present an extensive geo-tagged video dataset named GeoUGV that has been collected as part of the MediaQ [3] and GeoVid [1] projects. The key features of the dataset are that each video file is accompanied by a metadata sequence of geo-tags consisting of GPS locations, compass directions, and spatial keywords at fine-grained intervals. The GeoUGV dataset has been collected by volunteer users and its statistics can be summarized as follows: 2,397 videos containing 208,976 video frames that are geo-tagged, collected by 289 users in more than 20 cities across the world over a period of 10 years (2007-2016). We hope that this dataset will be useful for researchers, scientists and practitioners alike in their work.

Efficient and thorough data collection and its timely analysis are critical for disaster response and recovery in order to save peoples lives during disasters. However, access to comprehensive data in disaster areas and their quick analysis to transform the data to actionable knowledge are challenging. With the popularity and pervasiveness of mobile devices, crowdsourcing data collection and analysis has emerged as an effective and scalable solution. This paper addresses the problem of crowdsourcing mobile videos for disasters by identifying two unique challenges of 1) prioritizing visualdata collection and transmission under bandwidth scarcity caused by damaged communication networks and 2) analyzing the acquired data in a timely manner. We introduce a new crowdsourcing framework for acquiring and analyzing the mobile videos utilizing fine granularity spatial metadata of videos for a rapidly changing disaster situation. We also develop an analytical model to quantify the visual awareness of a video based on its metadata and propose the visual awareness maximization problem for acquiring the most relevant data under bandwidth constraints. The collected videos are evenly distributed to off-site analysts to collectively minimize crowdsourcing efforts for analysis. Our simulation results demonstrate the effectiveness and feasibility of the proposed framework.

An unprecedented number of user-generated videos (UGVs) are currently being collected by mobile devices, however, such unstructured data are very hard to index and search. Due to recent development, UGVs can be geo-tagged, e.g., GPS locations and compass directions, at the acquisition time at a very fine spatial granularity. Ideally, each video frame can be tagged by the spatial extent of its coverage area, termed Field-Of-View (FOV). In this paper, we focus on the challenges of spatial indexing and querying of FOVs in a large repository. Since FOVs contain both location and orientation information, and their distribution is non-uniform, conventional spatial indexes (e.g., R-tree, Grid) cannot index them efficiently. We propose a class of new R-tree-based index structures that effectively harness FOVs' camera locations, orientations and view-distances, in tandem, for both filtering and optimization. In addition, we present novel search strategies and algorithms for efficient range and directional queries on FOVs utilizing our indexes. Our experiments with a real-world dataset and a large synthetic video dataset (over 30 years worth of videos) demonstrate the scalability and efficiency of our proposed indexes and search algorithms and their superiority over the competitors.

Automatic reconstruction of 3D models is attracting increasing attention in the multimedia community. Scene recovery from video sequences requires a selection of representative video frames. Most prior work adopted content-based techniques to automate key frame extraction. However, these methods take no frame geo-information into consideration and are still compute-intensive. Here we propose a new approach for key frame selection based on the geographic properties of videos. Currently, an increasing number of user-generated videos (UGVs) are collected — a trend that is driven by the ubiquitous availability of smartphones. Additionally, it has become easy to continuously acquire and fuse various sensor data (e.g., geo-spatial metadata) with video to create geo-tagged mobile videos. Our novel technique utilizes these underlying geo-metadata to select the most representative frames. Specifically, a key frame subset with minimal spatial coverage gain difference is extracted by incorporating a manifold structure into reproducing a kernel Hilbert space to analyze the spatial relationship among the frames. Our experimental results illustrate that the execution time of the 3D reconstruction is shortened while the model quality is preserved.

MediaQ is a novel online media management system to collect, organize, share, and search mobile multimedia contents using automatically tagged geospatial metadata. User-generated-videos can be uploaded to the MediaQ from users' smartphones, iPhone and Android, and displayed accurately on a map interface according to their automatically sensed geospatial and other metadata. The MediaQ system provides the following distinct features. First, individual frames of videos (or any meaningful video segments) are automatically annotated by objective metadata which capture four dimensions in the real world: the capture time (when), the camera location and viewing direction (where), several key-words (what) and people (who). We term this data W4-metadata and they are obtained by utilizing camera sensors, geospatial and computer vision techniques. Second, a new approach of collecting multimedia data from the public has been implemented using spatial crowdsourcing, which allows media content to be collected in a coordinated manner for a specific purpose. Lastly, flexible video search features are implemented using W4 metadata, such as directional queries for selecting multimedia with a specific viewing direction.

GIS applications now increasingly make use of geo-located multimedia data such as images and videos. Furthermore, the wide-spread availablity of smartphones allows the acquisition of user-generated videos that are annotated with geo-properties. The sensor meta-data, e.g., GPS and digital compass values, are considerably smaller in size than the visual content and are helpful in effectively and efficiently manage and search through large repositories of videos. However, a major practical issue is the noisy nature of such sensor data. For example, due to sensor data inaccuracies the visual coverage described by the meta-data may not exactly match the actual video scene, which leads to imprecise search results and positional disagreements on map overlays. Obstructions between the camera and its captured objects make these situations worse. Therefore, robust error-tolerance is an essential feature of any geo-tagged video search application.

Tagging facilitates video search in many social media and web applications. While manual tagging is time consuming, subjective and sometimes inaccurate, auto-tagging facilitated by content-based techniques is compute-intensive and challenging to apply across domains. We have developed a complementary system, named SRV-TAGS, to automatically generate tags for outdoor videos based on their geographic properties, to index the videos based on their generated tags and to provide textual search services. The system works with our geo-referenced video management web portal, enabling users to manage, search and watch videos.

Video tag annotations have become a useful and powerful feature to facilitate video search in many social media and web applications. The majority of tags assigned to videos are supplied by users - a task which is time consuming and may result in annotations that are subjective and lack precision. A number of studies have utilized content-based extraction techniques to automate tag generation. However, these methods are compute-intensive and challenging to apply across domains. Here, we describe a complementary approach for generating tags based on the geographic properties of videos. With today's sensor-equipped smartphones, the location and orientation of a camera can be continuously acquired in conjunction with the captured video stream. Our novel technique utilizes these sensor meta-data to automatically tag outdoor videos in a two step process. First, we model the viewable scenes of the video as geometric shapes by means of its accompanied sensor data and determine the geographic objects that are visible in the video by querying geo-information databases through the viewable scene descriptions. Subsequently we extract textual information about the visible objects to serve as tags. Second, we define six criteria to score the tag relevance and rank the obtained tags based on these scores. Then we associate the tags with the video and the accurately delimited segments of the video. To evaluate the proposed technique we implemented a prototype tag generator and conducted a user study. The results demonstrate significant benefits of our method in terms of automation and tag utility.

Mobile devices are increasingly popular for the versatile capture and delivery of video content. However, the acquisition and transmission of large amounts of video data on mobile devices face fundamental challenges such as power and wireless bandwidth constraints. To support diverse mobile video applications, it is critical to overcome these challenges. We present a design framework that brings together several key ideas to enable energy-efficient mobile video management applications. First, we leverage off-the-shelf smartphones as mobile video sensors. Second, concurrently with video recordings we acquire geospatial sensor meta-data to describe the videos. Third, we immediately upload the meta-data to a server to enable low latency video search. This last step allows for very energy-efficient transmissions, as the sensor data sets are small and the bulky video data can be uploaded on demand, if and when needed. We present the design, a simulation study, and a preliminary prototype of the proposed system. Experimental results show that our approach substantially prolongs the battery life of mobile devices while only slightly increasing the search

Recently various sensors, such as GPS and compass devices, can be cost-effectively manufactured and this allows their deployment in conjunction with mobile video cameras. Hence, recorded clips can automatically be annotated with geospatial information and the resulting georeferenced videos may be used in various Geographic Information System (GIS) applications. However, the research community is lacking large-scale and realistic test datasets of such sensor-fused information to evaluate their techniques since collecting real-world test data requires considerable time and effort. To fill this void, we propose an approach for generating synthetic video meta-data with realistic geospatial properties for mobile video management research. We highlight the essential aspects of the georeferenced video meta-data and present an approach to simulate the behavioral patterns of mobile cameras in the synthetic data. The data generation process can be customized through user parameters for a variety of GIS applications that use mobile videos. We demonstrate the feasibility and applicability of the proposed approach by providing comparisons with real-world data.

Increasingly geographic properties are being associated with videos, especially those captured from mobile cameras. The meta data from camera-attached sensors can be used to model the coverage area of the scene as a spatial object such that videos can be organized, indexed and searched based on their field of views (FOV). The most accurate representation of an FOV is through the geometric shape of a circular sector. However, spatial search and indexing methods are traditionally optimized for rectilinear shapes because of their simplicity. Established methods often use an approximation shape, such as a minimum bounding rectangle (MBR), to efficiently filter a large archive for possibly matching candidates. A second, refinement step is then applied to perform the time-consuming, precise matching function. MBR estimation has been successful for general spatial overlap queries, however it provides limited flexibility for georeferenced video search. In this study we propose a novel vector-based model for FOV estimation which provides a more versatile basis for georeferenced video search while providing competitive performance for the filter step. We demonstrate how the vector model can provide a unified method to perform traditional overlap queries while also enabling searches that, for example, concentrate on the vicinity of the camera's position or harness its view direction. To the best of our knowledge no comparable technique exists today.

An increasing number of recorded videos are being tagged with geographic properties of the camera scenes. This meta-data is of significant use for storing, indexing and searching large collections of videos. By considering video related meta-information, more relevant and precisely delimited search results can be returned. Our system implementation demonstrates a prototype of a georeferenced video search engine (GRVS) that utilizes an estimation model of a camera's viewable scene for efficient video search. For video acquisition, our system provides an automated annotation software that captures videos and their respective field of views (FOV). The acquisition software allows community-driven data contributions to the search engine.

Video sensors are becoming ubiquitous and the volume of captured video material is very large. Therefore, tools for searching video databases are indispensable. Current techniques that extract features purely based on the visual signals of a video are struggling to achieve good results. By considering video related meta-information, more relevant and precisely delimited search results can be obtained. In this study we propose a novel approach for querying videos based on the notion that the geographical location of the captured scene in addition to the location of a camera can provide valuable information and may be used as a search criterion in many applications. This study provides an estimation model of the viewable area of a scene for indexing and searching and reports on a prototype implementation. Among our objectives is to stimulate a discussion of these topics in the research community as information fusion of different georeferenced data sources is becoming increasingly important. Initial results illustrate the feasibility of the proposed approach.

Many Geographic Information Systems (GIS) handle large geospatial datasets stored in raster representation. Spatial joins over raster data are important queries in GIS for data analysis and decision support. However, evaluating spatial joins can be very time intensive due to the size of these datasets. In this paper we propose a new interactive framework that allows users to get approximate answers in near instantaneous time, thus allowing for truly interactive data exploration. Our method utilizes two proposed statistical approaches: probabilistic join and sampling based join. Our probabilistic join method provides speedup of two orders of magnitude with no correctness guarantee, while our sampling based method provides an order of magnitude improvement over the full quad-tree join and also provides running confidence intervals. We propose a framework that combines the two approaches to allow end users to tradeoff speed versus bounded accuracy. The two approaches are evaluated empirically with real and synthetic datasets.

The RAID (Redundant Array of Inexpensive Disks) system has been widely used in practical storage applications for better performance, cost effectiveness, and reliability. This study proposes a novel variant of RAID named Zoned-RAID (Z-RAID). Z-RAID improves the performance of traditional RAID by utilizing the zoning property of modern disks which provides multiple zones with different data transfer rates within a disk. Z-RAID levels 1, 5, and 6 are introduced to enhance the effective data transfer rate of RAID levels 1, 5, and 6, respectively, by constraining the placement of data blocks in multizone disks. We apply the Z-RAID to a practical and popular application, streaming media server, that requires a high-data transfer rate as well as a high reliability. The analytical and experimental results demonstrate the superiority of Z-RAID to conventional RAID. Z-RAID provides a higher effective data transfer rate in normal mode with no disadvantage. In the presence of a disk failure, Z-RAID still performs as well as RAID.

Researches on the continuous media (CM) servers have focused on the throughput issues because the most popular CM application has been the video-on-demand system which provides relatively long video clips or movies. Latency issue has received little atten-tion in such a system because a few seconds up to a few minutes latency is considered tolerable. However, as a variety of multime-dia applications are emerging, some applications begin to require interactive presentation of continuous media where the most chal-lenging issue is to minimize startup latency, i.e., minimizing the time between the issuing a request and the start of the display. This paper proposes bulk prefetching techniques to minimize startup latency and analyzes their performance on multi-disk CM servers utilizing a deadline-driven scheduling and random data placement. Simulation results demonstrate a fraction of a second startup la-tency, which is acceptable for most interactive applications. 1.

A number of recent technological trends have made data intensive applications such as continuous media (audio and video) servers a reality. These servers are expected to play an important role in applications such as video-on-demand, digital library, news-on-demand, distance learning, etc. Continuous media applications are data intensive and might require storage subsystems that consist of hundreds of (multi-zone) disk drives. With the current technological trends, a homogeneous disk subsystem might evolve to consist of a heterogeneous collection of disk drives. Given such a storage subsystem, the system must continue to support a hiccup-free display of audio and video clips. This study describes extensions of four continuous display techniques for multi-zone disk drives to a heterogeneous platform. These techniques include IBM's Logical Track [21], HP's Track Pairing [4], and USC's FIXB [9] and deadline driven techniques [10]. We quantify the performance tradeoff associated with these techniques using analytical models and simulation studies. The obtained results demonstrate tradeoffs between the cost per simultaneous stream supported by a technique, the wasted disk space, and the incurred startup latency.

The past decade has witnessed a proliferation of repositories that store and retrieve continuous media data types, e.g., audio and video objects. These repositories are expected to play a major role in several emerging applications, e.g., library information systems, educational applications, entertainment industry, etc. To support the display of a video object, the system partitions each object into fixed size blocks. All blocks of an object reside permanently on the disk drive. When displaying an object, the system stages the blocks of the object into memory one at a time for immediate display. In the presence of multiple displays referencing different objects, the bandwidth of the disk drive is multiplexed among requests, introducing disk seeks. Disk seeks reduce the useful utilization of the disk bandwidth and result in a lower number of simultaneous displays (throughput).This paper characterizes the impact of disk seeks on the throughput of the system. It describes REBECA as a mechanism that maximizes the throughput of the system by minimizing the time attributed to each incurred seek. A limitation of REBECA is that it increases the latency observed by each request. We quantify this throughput vs latency tradeoff of REBECA and, develop an efficient technique that computes its configuration parameters to realize the performance requirements (desired latency and throughput) of an application.