Although the evolution of Brazilian coastal depositional systems in the Quaternary has been studied in past decades, it is only in the last couple of years that it has been possible to incorporate the latest remote sensing databases available to help understand their development. In comparison to other freely accessible imagery, high-resolution images available on Google Earth are advantageous when undertaking local coastal analysis. In some instances, it is possible to differentiate geomorphologic features such as tidal deltas, beach ridges, and dunes. Also, the monitoring of small-scale features allows evaluation of the sensitivity of coastal zones to high-frequency and low-intensity processes. Thus, the downscaling description of coastal zones is now easily accessible, permitting the analysis of the extensive Brazilian coastal depositional systems. On the regional scale, a quick glance of a coastal setting may help frame the sedimentary characteristics of the depositional system. Coastal areas in the States of Santa Catarina and São Paulo are taken into consideration in this study. These areas illustrate representative prograded barrier formations from Middle to Late Holocene with dunes formed at a later development stage. A comparison is made in the use of Google Earth and its historic images with aerial photographs and Landsat images. In the past, small-scale features of these regions were evaluated in aerial photographs, while regional features were studied by low-resolution satellite images. Accordingly, integration of these two products was difficult. In this work, we show that Google Earth facilitates the analysis as a whole. Furthermore, comparison of Google Earth images with aerial photographs from 1938 onward allowed the study of short-term migration and deflation of the dunefields probably accelerated in recent years by human interference. In addition, Keyhole Markup Language (KML) files were saved from Google Earth placemarks to facilitate georeferencing raster images on GIS programs. Finally, information available from previous local studies, such as luminescence dating, geomorphology of the costal system, grain size, heavy minerals, pollen, and carbon isotope analyses, was gathered into a Google Fusion Table database making data retrieval and parsing easily accessible. This database provides information that can be shared with other researchers and may be used to address important questions about the development of Brazil's coastal system in the past, present, and future.

Keyhole Markup Language (KML)—a type of extensible markup language (XML)—is the key to the extensibility of Google Earth for geoscience applications. Static KML code may be saved to a file from the Google Earth desktop application, handwritten with a text editor, or generated by running a custom computer program. Many Google Earth visualizations are limited to static KML developed with the desktop application's user interface. The purpose of this paper is to highlight how much more is possible with the implementation of additional applications. Geoscience learning resources may be taken to the next level with the interactive generation and animation of graphics and models both in the desktop application and using the Google Earth web browser plug-in and its JavaScript application programing interface. Dynamic KML may be generated on-the-fly by means of client-side or server-side scripts, or with the aid of Google Fusion Tables and network links.

Data on ground-water and surface-water use in Louisiana are available online in tabular form from the U.S. Geological Survey. Data are categorized by parish and by type of usage (e.g., public supply, irrigation, industry, and power generation) from 1960 to 2005. Water usage in Louisiana has complicated spatial and temporal trends which are not readily apparent in static tables. For example, ground-water usage varies from more than 200 million gallons a day in some rice farming parishes to less than 40,000 gallons a day in coastal parishes where most ground water is not potable. Baton Rouge Parish uses mostly ground water even though it is on the Mississippi River because the ground water is high quality. Orleans Parish uses almost exclusively river water because most ground water is brackish. Significant temporal trends include the rapid rise of water use for power generation since 1960, a drop in overall water usage during the economic downturn following the oil bust of the 1980s, and the switch from surface to ground water in some areas due to decadal droughts or pollution of surface water. Google Fusion Tables represent a rapid and effective way to visualize water usage trends for K–16 education, research, and public policy. Using Google API (application programming interface), we have developed intensity maps that illustrate quantity and category of both surface-water and ground-water use by parish. Each parish within an intensity map has a pop-up bar chart that shows total water usage from 1960 to 2005 in five-year increments. We also have included versions of intensity maps that have pop-up pie/line charts that show the distribution of usage in each parish among public supply, agriculture, industry, and power generation. The dynamic feature of Fusion Tables allows students, researchers, and policy makers to clearly see temporal trends as well as illustrate connections among water usage and other factors. For examples, most students falsely assume that the steady rise in water use for public supply is related to population increase whereas it is primarily due to a substantial increase in per capita usage. These tables will be made available on the web.

The introductions and road logs from field trips offered by the New York State Geological Association (NYSGA) over the past 55 years are being transformed into kml files. These files are maintained as Google Fusion Tables, accessible to the public. This paper begins by briefly summarizing the kinds of data being transformed, their strengths, and their limitations. It then details the procedures used to accomplish the transformation, from scanning the original document to uploading the data to Fusion Tables. By using a subset of available kml (Keyhole Markup Language) fields, and establishing a numbering convention for the placemarks, an efficient system has been developed where sufficient metadata is embedded within each placemark to permit mixing and matching of any of the placemarks. Using this system, additional field trip guides from GSA (Geological Society of America), AAPG (American Association of Petroleum Geologists), NEIGC (New England Inter-Collegiate Geological Conference), etc., might be transformed, increasing the size and value of the Fusion Tables database. The information provided will permit others to do this, producing kml files and Fusion Tables which will be consistent with those already done. The paper discusses searching, merging, and adding photos to Fusion Tables and some of the ways in which Fusion Table data can be displayed on websites dynamically. The paper concludes by describing how the Fusion Tables from this project can export custom-made field trips, can be manipulated by other GIS applications, and can be used in a classroom setting to produce crude geologic maps.