Ireland ESRI Residential Property Rent: Dublin: Houses data was reported at 1,478.000 EUR in Sep 2016. This records a decrease from the previous number of 1,487.000 EUR for Jun 2016. Ireland ESRI Residential Property Rent: Dublin: Houses data is updated quarterly, averaging 1,238.000 EUR from Sep 2007 (Median) to Sep 2016, with 37 observations. The data reached an all-time high of 1,487.000 EUR in Jun 2016 and a record low of 1,113.000 EUR in Mar 2011. Ireland ESRI Residential Property Rent: Dublin: Houses data remains active status in CEIC and is reported by The Economic and Social Research Institute. The data is categorized under Global Database’s Ireland – Table IE.EB004: ESRI Standardised Monthly Residential Property Rent.

The German geospatial imagery analytics market is experiencing robust growth, projected to reach €0.62 billion in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 26.77% from 2025 to 2033. This expansion is driven by several key factors. Increasing government investments in infrastructure development and smart city initiatives fuel demand for precise geospatial data analysis. Furthermore, the agricultural sector's adoption of precision farming techniques leveraging imagery analytics for yield optimization and resource management contributes significantly to market growth. The insurance industry also utilizes this technology for risk assessment and claims processing, boosting market demand. Advancements in sensor technology, providing higher-resolution imagery and improved data processing capabilities, further accelerate market expansion. The increasing availability of cloud-based solutions offers scalability and cost-effectiveness, making geospatial imagery analytics accessible to a broader range of organizations, from SMEs to large enterprises. While data privacy concerns and the need for skilled professionals represent potential restraints, the overall market outlook remains exceptionally positive. The market segmentation reveals a dynamic landscape. Cloud deployment is anticipated to dominate due to its inherent flexibility and scalability advantages. Within the type segment, video analytics is projected to witness faster growth than imagery analytics, driven by the rising need for real-time monitoring and analysis in various sectors such as security and environmental monitoring. Large enterprises currently hold a larger market share compared to SMEs; however, increased awareness and affordability of cloud-based solutions are likely to drive significant growth within the SME segment in the coming years. The defense and security, and environmental monitoring verticals are expected to lead market adoption due to the critical nature of their applications. Companies such as Hexagon AB, Esri Deutschland GmbH, and Airbus SE are key players, driving innovation and competition within the German market. The consistent growth trajectory suggests that the German geospatial imagery analytics market will remain a lucrative investment opportunity throughout the forecast period. Recent developments include: January 2024 - LiveEO, a Berlin-based Earth observation scaleup company that specializes in using AI to analyze Earth observation data in support of critical transport and energy infrastructure, launched its EUDR Expert. It is an AI compliance advisor solution that helps understand the complexities and challenges that clients face with the EU Deforestation Regulation (EUDR)., September 2023 - European Space Imaging (EUSI), a provider of very high resolution (VHR) optical satellite imagery, partnered with Umbra, a company in advanced space radar technology. This partnership aids customers in buying Umbra’s synthetic aperture radar (SAR) data directly through EUSI across Europe, including Germany, increasing the availability of geospatial imagery data and creating a market growth opportunity for analytics software.. Key drivers for this market are: The Growth of Infrastructure Development and Urban Planning in the Country, The Growing Demand for High-resolution Satellite Data for Crisis Response, Environmental Monitoring, and Nature Conservation Efforts. Potential restraints include: The Growth of Infrastructure Development and Urban Planning in the Country, The Growing Demand for High-resolution Satellite Data for Crisis Response, Environmental Monitoring, and Nature Conservation Efforts. Notable trends are: Imagery Analytics Contributes Significantly to the Market Share.

O mapa de cobertura de terra do BNETD 2020 da Costa do Marfim foi produzido pelo governo do país por meio de uma instituição nacional, o Centro de Informações Geográficas e Digitais do Escritório Nacional de Estudos e Desenvolvimento (BNETD-CIGN, na sigla em inglês), com apoio técnico e financeiro da União Europeia. A metodologia usada para produzir o mapa foi transparente, participativa e alinhada com os padrões internacionais. Para desenvolver esse mapa, um mosaico de imagens de satélite (Sentinel 2) de 2020 foi processado pelo Google Earth Engine e complementado com dados coletados no campo para treinar um algoritmo de classificação supervisionado (Random Forest). Duas campanhas de campo foram realizadas, de 10 de novembro a 9 de dezembro de 2022 e de 26 de janeiro a 13 de fevereiro de 2023, em todo o país. Essas missões envolveram 33 pessoas de várias organizações parceiras, porque os métodos de coleta de dados e as definições de determinadas classes de uso da terra adotadas pelas partes interessadas às vezes podem ser diferentes. Como parte do processo de diligência da EUDR, os dados de geolocalização de terrenos que produzem produtos relevantes para a EUDR podem ser sobrepostos aos dados de cobertura florestal de 2020 para avaliar o risco de o terreno estar localizado em uma área que tinha floresta antes da data limite de 2020. Para isso, são necessários dados de cobertura florestal alinhados à definição de florestas da FAO e a data limite de 2020. O mapa de cobertura da terra de 2020 da Costa do Marfim atende a essas necessidades. De fato, as classes no mapa de cobertura da terra podem ser combinadas para criar um mapa de floresta/não floresta alinhado com a definição de florestas da FAO. Uma plataforma para acessar dados de cobertura de terras de 2020, metadados e a metodologia foi desenvolvida usando soluções ESRI, do GeoPortal da África, para análise e visualização de dados: O endereço é: https://bit.ly/carte-ci-2020 Documentação: Documentação detalhada Metodologia em francês