# Review of Web Mapping: Eras, Trends and Directions
## 3 Web Mapping Eras of Development
### Web Mapping Eras
#### 3.2.8 Cloud Web Mapping Era  
The _cloud web mapping era_ focuses on the availability and scalability of increasing masses of information, with the cloud providing a solution. Now storage, software, services and infrastructure can be provided on the web in the cloud as services, providing a measure of reliability, stability and scalability for information, applications and users. This era was identified by [PERSON] [28] as part of a fifth generation of web mapping technology, following on from [PERSON]'s [27] four generations. Cloud technology provides resource pooling, virtualized applications and a shared platform from which mapping applications and databases can be built, integrated and shared on the web. Examples include for example, Amazon's cloud storage and web services, Microsoft Azure cloud, ESRI ArcGIS Online, GeoNode, GIS Cloud, and CartoDB. Cloud computing supports the expansion of the digital earth technologies as well as complex geospatial applications [76]. It encourages rich Internet applications (RIA) which refers to a web development environment that provides a friendly user interface and access to powerful widgets and tools. Examples include Google Web Toolkit and Adobe FLEX. The cloud also provides a platform to manage and manipulate big data, which is a characteristic of the growing volumes of geospatial data being collected through sensors and mobile devices [77]. Emerging platforms, such as the open source Hadoop [78], Hive [79], and MongoDB [80], provide distributed and parallel computing environments to perform geospatial big data analytics. The cloud computing is based on a service-oriented architecture, where \"everything is a service\". This concept is summarized in the acronym XaaS, which stands for Everything-as-a-Service and includes the cloud services models [81]:  
* Software as a Service (SaaS)--users with a (thin) client via a web browser to use the provider's applications running on a cloud infrastructure. Example of SaaS is the GIS as a Service, characterized by management and integration of GIS workflows, in case with BI (BusinessIntelligence) integration. A second example can be that of Image as a Service provided by the ESA Sentinel Application Platform, SNAP [82]. * Platform as a Service (PaaS)--the users are provided with a full development platform where they can create, run, and host their own applications. A typical example is the Google App Engine that provides users the capability to develop their applications using Python, Java, PHP, etc. * Infrastructure as a Service (IaaS)--where users are on demand offered by service providers compute resources, storage and networking capabilities. Such architectures include Amazon Web Services (AWS), Windows Azure, Google Compute Engine, Rackspace Open Cloud, and IBM SmartCloud Enterprise. For example, exploiting such an infrastructure (in the specific case Google Compute Engine), Descartes Labs is able to process huge amount of images from the NASA and other satellites to analyze variables such as the growth and health of crops, the growth of cities, the spread of forest fires and the state of available drinking water across the globe [83]. Cloud web mapping is obviously general purpose and relevant examples in many fields can be found. Mapstory [84], for example, is an infrastructure for \"Map Story telling\", enabling students, teachers or practitioners to convey their stories using maps and time as glue for their narration. Harvard WorldMap [85] is an online cloud-mapping platform developed for educational purposes. Masdap [86] is a public platform for GIS data to support development in Malawi. A relevant aspect to be mentioned referring to the cloud web mapping is the Internet of Things (IoT): millions to billions of sensors directly or indirectly connected to the Internet [87]. IoT is going to generate an enormous amount of data and the cloud is currently the only way for efficiently storing, processing and analyzing these data [88].