Deep learning in environmental remote sensing: Achievements and challenges

As preservers of history and the objects that others leave behind, it is easy to get distracted by a desire for those objects to be “perfect.” We worry about every little tear, stain or blemish, and we want nothing more than to return objects to their "original state." However, so many times those rough spots are part of the item’s history. It is important to understand the difference between the types of condition issues on objects that cause structural issues versus those that are purely cosmetic. Knowing and identifying these differences can help when determining whether or not an item requires conservation or deciding simple factors like the best way to store or exhibit an item.

Facebook“卖流量”新招

Measuring spatial accessibility to primary care in rural areas: Improving the effectiveness of the two-step floating catchment area method

Advances in Earth Observation (EO) technology, particularly over the last two decades, have shown that soil moisture content (SMC) can be measured to some degree or other by all regions of the electromagnetic spectrum, and a variety of techniques have been proposed to facilitate this purpose.

In this review we provide a synthesis of the efforts made during the last 20 years or so towards the estimation of surface SMC exploiting EO imagery, with a particular emphasis on retrievals from microwave sensors. Rather than replicating previous overview works, we provide a comprehensive and critical exploration of all the major approaches employed for retrieving SMC in a range of different global ecosystems. In this framework, we consider the newest techniques developed within optical and thermal infrared remote sensing, active and passive microwave domains, as well as assimilation or synergistic approaches. Future trends and prospects of EO for the accurate determination of SMC from space are subject to key challenges, some of which are identified and discussed within.

It is evident from this review that there is potential for more accurate estimation of SMC exploiting EO technology, particularly so, by exploring the use of synergistic approaches between a variety of EO instruments. Given the importance of SMC in Earth’s land surface interactions and to a large range of applications, one can appreciate that its accurate estimation is critical in addressing key scientific and practical challenges in today’s world such as food security, sustainable planning and management of water resources. The launch of new, more sophisticated satellites strengthens the development of innovative research approaches and scientific inventions that will result in a range of pioneering and ground-breaking advancements in the retrievals of soil moisture from space.

Surface soil moisture retrievals from remote sensing: Current status, products & future trends

The geospatial sciences face grand information technology (IT) challenges in the twenty-first century: data intensity, computing intensity, concurrent access intensity and spatiotemporal intensity. These challenges require the readiness of a computing infrastructure that can: (1) better support discovery, access and utilization of data and data processing so as to relieve scientists and engineers of IT tasks and focus on scientific discoveries; (2) provide real-time IT resources to enable real-time applications, such as emergency response; (3) deal with access spikes; and (4) provide more reliable and scalable service for massive numbers of concurrent users to advance public knowledge. The emergence of cloud computing provides a potential solution with an elastic, on-demand computing platform to integrate – observation systems, parameter extracting algorithms, phenomena simulations, analytical visualization and decision support, and to provide social impact and user feedback – the essential eleme...

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2011.587547

Spatial cloud computing: how can the geospatial sciences use and help shape cloud computing?

Routine surveillance of disease notification data can enable the early detection of localised disease outbreaks. Although hidden Markov models (HMMs) have been recognised as an appropriate method to model disease surveillance data, they have been rarely applied in public health practice. We aimed to develop and evaluate a simple flexible HMM for disease surveillance which is suitable for use with sparse small area count data and requires little baseline data. A Bayesian HMM was designed to monitor routinely collected notifiable disease data that are aggregated by residential postcode. Semi-synthetic data were used to evaluate the algorithm and compare outbreak detection performance with the established Early Aberration Reporting System (EARS) algorithms and a negative binomial cusum. Algorithm performance varied according to the desired false alarm rate for surveillance. At false alarm rates around 0.05, the cusum-based algorithms provided the best overall outbreak detection performance, having similar sensitivity to the HMMs and a shorter average time to detection. At false alarm rates around 0.01, the HMM algorithms provided the best overall outbreak detection performance, having higher sensitivity than the cusum-based Methods and a generally shorter time to detection for larger outbreaks. Overall, the 14-day HMM had a significantly greater area under the receiver operator characteristic curve than the EARS C3 and 7-day negative binomial cusum algorithms. Our findings suggest that the HMM provides an effective method for the surveillance of sparse small area notifiable disease data at low false alarm rates. Further investigations are required to evaluation algorithm performance across other diseases and surveillance contexts.

/pdf/disease-surveillance-using-a-hidden-markov-model-4ty20ih0jz.pdf

Disease surveillance using a hidden Markov model

Web mapping and the use of geospatial information online have evolved rapidly over the past few decades. Almost everyone in the world uses mapping information, whether or not one realizes it. Almost every mobile phone now has location services and every event and object on the earth has a location. The use of this geospatial location data has expanded rapidly, thanks to the development of the Internet. Huge volumes of geospatial data are available and daily being captured online, and are used in web applications and maps for viewing, analysis, modeling and simulation. This paper reviews the developments of web mapping from the first static online map images to the current highly interactive, multi-sourced web mapping services that have been increasingly moved to cloud computing platforms. The whole environment of web mapping captures the integration and interaction between three components found online, namely, geospatial information, people and functionality. In this paper, the trends and interactions among these components are identified and reviewed in relation to the technology developments. The review then concludes by exploring some of the opportunities and directions.

/pdf/review-of-web-mapping-eras-trends-and-directions-porq6tmfjf.pdf

Review of Web Mapping: Eras, Trends and Directions

The automated monitoring of routinely collected disease surveillance data has the potential to ensure that important changes in disease incidence are promptly recognised. However, few studies have established whether the signals produced by automated monitoring methods correspond with events considered by epidemiologists to be of public health importance. This study investigates the correspondence between retrospective epidemiological evaluation of notifications of Ross River virus (RRv) disease in Western Australia, and the signals produced by two cumulative sum (cusum)-based automated monitoring methods. RRv disease case notification data between 1991 and 2004 were assessed retrospectively by two experienced epidemiologists, and the timing of identified outbreaks was compared with signals generated from two different types of cusum-based automated monitoring algorithms; the three Early Aberration Reporting System (EARS) cusum algorithms (C1, C2 and C3), and a negative binomial cusum. We found the negative binomial cusum to have a significantly greater area under the receiver operator characteristic curve when compared with the EARS algorithms, suggesting that the negative binomial cusum has a greater level of agreement with epidemiological opinion than the EARS algorithms with respect to the existence of outbreaks of RRv disease, particularly at low false alarm rates. However, the performance of individual EARS and negative binomial cusum algorithms were not significantly different when timeliness was also incorporated into the area under the curve analyses. Our retrospective analysis of historical data suggests that, compared with the EARS algorithms, the negative binomial cusum provides greater sensitivity for the detection of outbreaks of RRv disease at low false alarm levels, and decreased timeliness early in the outbreak period. Prospective studies are required to investigate the potential usefulness of these algorithms in practice.

/pdf/applying-cusum-based-methods-for-the-detection-of-outbreaks-4oiym1pk7f.pdf

Applying cusum-based methods for the detection of outbreaks of Ross River virus disease in Western Australia

Passive microwave remote sensing is one of the most promising techniques for soil moisture retrieval. However, the inversion of soil moisture from brightness temperature observations is not straightforward, as it is influenced by numerous factors such as surface roughness, vegetation cover, and soil texture. Moreover, the relationship between brightness temperature, soil moisture and the factors mentioned above is highly non-linear and ill-posed. Consequently, Artificial Neural Networks (ANNs) have been used to retrieve soil moisture from microwave data, but with limited success when dealing with data different to that from the training period. In this study, an ANN is tested for its ability to predict soil moisture at 1 km resolution on different dates following training at the same site for a specific date. A novel approach that utilizes information on the variability of soil moisture, in terms of its mean and standard deviation for a (sub) region of spatial dimension up to 40 km, is used to improve the current retrieval accuracy of the ANN method. A comparison between the ANN with and without the use of the variability information showed that this enhancement enables the ANN to achieve an average Root Mean Square Error (RMSE) of around 5.1% v/v when using the variability information, as compared to around 7.5% v/v without it. The accuracy of the soil moisture retrieval was

/pdf/use-of-soil-moisture-variability-in-artificial-neural-4t67jzfhgq.pdf

Use of Soil Moisture Variability in Artificial Neural Network Retrieval of Soil Moisture

Communications network damage resulting from a large disaster causes difficulties in the ability to rapidly understand the current situation and thus make appropriate decisions towards mitigating problems, such as where to send and dispense emergency supplies. The research outlined in this paper focuses on the rapid construction of a network after a disaster occurs. This study suggests ZigBee and geographic information systems (GIS) technologies to resolve these problems and provide an effective communication system. The experimental results of the ZigBee network system are presented, examples are provided of the mapping and analysis undertaken using GIS for the disaster-stricken area of Tsukuba City, Japan, and the communications node arrangements are determined for this region. These results demonstrate the effectiveness of establishing such a communications system for supporting efforts to relieve disaster-damaged areas.

Bert Veenendaal

Papers

Disease surveillance using a hidden Markov model

Review of Web Mapping: Eras, Trends and Directions

Applying cusum-based methods for the detection of outbreaks of Ross River virus disease in Western Australia

Use of Soil Moisture Variability in Artificial Neural Network Retrieval of Soil Moisture

Using GIS to develop a mobile communications network for disaster-damaged areas