Imaging spectrometer record continuous reflectance and emission spectra in high spectral resolution and consequently resolve individual absorption bands which would remain undetected in broad-band multispectral imagery. Such data therefore nicely complements data from other sensor modalities such as multi-spectral scanner, Radar and Lidar. Unfortunately, however, the current temporal revisit frequency is insufficient as only a few orbital platforms have been launched.

In my talk, I will show a few successful applications derived from hyperspectral data cubes. This will include examples using both empirical approaches as well as physical-based approaches. I will discuss limitations of both approaches when analyzing data from imaging spectrometer with a particular focus on the ill-posed inverse problem, model transferability, as well as epistemic uncertainty as a result of model over-simplifications. I will conclude by presenting some recent progress in solving these issues.

Inland surface water is the most accessible water resource, of fundamental importance in several respects, at first freshwater supply for people and other living beings, agriculture and natural environment, industry and other human activities.

Its availability is threatened by climate change and its continuous monitoring worldwide is becoming of higher and higher relevance, being directly connected to the main goals of two UN SDGs, 6 (Clean water and sanitation) and 13 (Climate action).

It is therefore not surprising that a growing attention is reserved to the inland surface water monitoring through remote sensing sensors and methods, being this monitoring focused on the variation of the volume of the available water resources and not only on their extension, as already successfully done in the past at least from the launch of the Landsat missions in 1972: a 3D instead a 2D monitoring is nowadays in the spotlight.

To this aim, the Surface Water and Ocean Topography (SWOT) mission, whose spacecraft was placed in the final orbit in July 2023, and the Global Ecosystem Dynamics Investigation (GEDI) mission, reactivated onboard the International Space Station in December 2024, will play a key role.

Here methodologies are proposed to routinely process GEDI and SWOT data, based on thorough investigations on GEDI data collected in the period 2019-2023 and a preliminary investigation on SWOT data collected in 2024, highlighting the problems and potentials of these data and the open challenges to face with in the coming future.

The work deals with the use of satellite radar interferometric techniques for the analysis and monitoring of ground instability in urban areas. Some various space-borne MTInSAR-based procedures by exploiting SAR data from LEO Satellites Sentinel-1 and COSMO-SkyMed constellations have been applied to italian built-up sites, at different scales from the screening of the urban fabric up to a building-scale analysis. In particular, a procedure on the assessment of the urban instability by PSI (Persistent Scatterers Interferometry)-based classification indexes has been tested in Orvieto 100 km north of Rome; a methodology for urban territory screening by means of PSI time series displacements has been performed on Pienza historical town that is included in the UNESCO WHL (World Heritage list); a procedure for generating empirical fragility and vulnerability curves from InSAR ground deformation rates and field damage survey was has been proposed for buildings exposed to slow-moving landslides on urban masonry settlements in Northern Appennine; a building deformation assessment for deriving differential settlement parametershas been applied in Volterra urban area in Central Italy. A short discussion on the relevant key points of each methodology is tacked, along with conclusions and future perspectives.

Using remote sensing (RS) and explainable AI (XAI) to enhance disaster risk management (DRM) and disaster risk reduction (DRR) presents several challenges. RS provides vast amounts of geospatial data, but integrating it with AI models requires addressing data quality, resolution, and timeliness issues. XAI, while improving transparency in AI decision-making, struggles with balancing complexity and interpretability, especially in high-stakes disaster scenarios.

A key challenge is the lack of labeled data for training AI models, as disaster events are rare and diverse. Additionally, RS data often contains noise and requires preprocessing, which can introduce biases. XAI models must also be tailored to non-expert stakeholders, such as emergency responders, to ensure actionable insights.

Furthermore, integrating RS and XAI into existing DRM frameworks requires overcoming technical, infrastructural, and institutional barriers. Ethical concerns, such as data privacy and algorithmic bias, also need addressing. Despite these challenges, combining RS and XAI holds promise for improving disaster preparedness, response, and recovery, provided these issues are systematically addressed.

Big data from space is providing massive spatio-temporal Earth Observation data to detect, monitor, and assess in almost real time geo-hazards threatening coastal areas especially those with high population density and infrastructure. However, the complexity of the problem demands new research approaches that effectively integrate Earth Sciences and Disaster Science to better 1) understand the complexity of processes underlying geo-hazards, 2) evaluate, model and forecast compound hazard risks, and 3) design and implement better disaster risk management policies and programs. In this talk I will present key achievements of Earth Observation methods in Earth and Disaster sciences and discuss common challenges and opportunities for rapid mapping and assessment of hazard risk in a range of applications. Example applications will also illustrate the compound effects of multi-hazards that are increasingly happening due to climate change. They include coastal flooding, land subsidence and earthquake damage mapping that may occur simultaneously or in sequence and constitute an increased threat especially to coastal communities

Rapid urbanization is transforming cities. Cities are challenged by severe housing and environmental issues, further exacerbated by the growing impacts of climate change. The problem of spatial inequalities is especially acute in cities of Low- and Middle-Income Countries (LMICs), although it is also present in High-Income Countries (HICs). To address these challenges, local adaptation strategies need to be underpinned by high-quality, timely, and reliable data that is detailed and specific to the local context. For this purpose, innovations in Earth Observation (EO) techniques, Artificial Intelligence (AI) methodologies, and information built together with local stakeholders need to be combined. This keynote offers an overview of various methodological advances and challenges, ranging from local to global scales, that combine EO/AI with Citizen Science approaches to facilitate evidence-based policymaking for sustainable development, in line with the Sustainable Development Goals (SDGs). For instance, in promoting climate adaptation, local discussions must be informed by detailed and reliable data on urban deprivation and climate impacts (e.g., floods, heat). Such data needs to consider social, economic, and environmental aspects to ensure comprehensive and effective adaptation measures. However, there are major challenges to fully realize the potential of EO data analysis. These challenges can be summarized as (a) Lack of in situ data availability, (b) Knowledge divide – most EO studies focus on the Global North, and c) Limited inclusion of local stakeholders in the development of mapping applications (limiting the relevance of the information). Much of the EO-based mapping is done without the inclusion and partnership with stakeholders living in mapped areas. The keynote will highlight advances in GeoAI-based methods for measuring urban inequalities across multiple dimensions (environmental, demographic, socio-economic, technological, hazards, climate change). It highlights novel datasets, societal relevance, and methodological innovations that provide locally relevant insights and societal impact.

The integration of drone technology, digital twins, artificial intelligence, and advanced simulations is revolutionising the predictive and prescriptive management of natural disasters. This keynote will explore how aerial drones are transforming wildfire and flood prevention through high-resolution geospatial data, risk modelling, and automated decision-support systems.

For wildfires, drone-based remote sensing enables the creation of detailed forest cartography, including AI-driven algorithms for extracting tree count, height, and vegetation continuity—key parameters for assessing fire spread potential. Additionally, risk categorization of wildland-urban interfaces (WUI) and fire propagation simulations provide crucial insights into managing strategic areas.

In flood risk management, drones are instrumental in detecting river obstacles, categorising their hazard levels, and generating actionable flood risk reports. These reports empower decision-makers to implement targeted preventive actions, such as debris removal and riverbed management, reducing the impact of future floods.

Through real-world case studies and technological breakthroughs, this keynote will highlight how drones, digital twins and geospatial intelligence are reshaping disaster prevention—moving from reactive approaches to proactive, data-driven strategies that safeguard ecosystems and communities.

The technological development of sensors and electronic devices in the last decade has allowed the acquisition of higher resolution and larger datasets with drone platforms in several applications. Despite the initial challenges in handling larger amounts of data, this has paved the way for developing more advanced data processing techniques and delivering higher quality and (near) real-time information to end-users.

In this presentation, some of these solutions will be presented, showing how the integration of different domains, such as remote sensing, artificial intelligence and big data analytics, has led to efficient products in several contexts. Urban monitoring, road and infrastructure inspection, indoor mapping and patrolling for search and rescue are some of the applications that will be considered. It will also be presented how the combination of edge and remote processing, or the integration of “hand-crafted” and deep learning techniques, can often be the solution to deliver more efficient and fit-for-purpose solutions.

Earth is experiencing great change and great uncertainty presently, not only its climate and environment, but its people, politics and conflict. Environmental agendas are tumbling down the order of priorities, as self-interest, colonialism and isolationism rise to the surface. There is a grave danger that the sceptic lobby will seize the opportunity to overturn decades of environmental progress and steer us back to fossil fuel addiction.

To counter this, we need simple, understandable and convincing evidence of environmental change, and effective communication of this evidence to citizens around the world. We have the evidence – our 50+ year archive of civilian remote sensing imagery, starting with Landsat, but multiplying into numerous missions, technologies and formats of data. The environment is changing; it’s hard to argue with a perfectly painted picture.

The challenge now is to get this evidence to the people who matter, starting with the general public, who can apply pressure to legislators and businesses. The evidence must not only be academic papers and IPCC reports, which might well yield zero or even negative responses. The expert remote sensing community must now, like a map, simplify the problem and deliver intuitive images of environmental change and the threats that lie ahead. And we must go beyond our traditional academic channels and exploit, and respond nimbly to, the changing media landscape. Problems with leading social media apps are well known; there are plenty of alternatives, currently BlueSky, Mastodon, Reddit…

The use of low-cost sensors and/or transpiration models to predict plant water needs has proven to be particularly useful for water and fertilizer saving, especially in greenhouse ornamental and vegetable crops established in the Mediterranean region. However, various challenges, such as sensor faults and insufficient model calibration, must be addressed when the above-mentioned methods are used to obtain reliable estimations. In this study, i) a specially designed multifunctional scale (manufactured using 3D printing) with a 1 g precision load cell connected to an Arduino microcontroller, ii) environmental sensors (light, temperature, and humidity), and iii) transpiration and evaporation models were combined through appropriate software to optimize the irrigation of potted gardenia plants grown in a Mediterranean greenhouse. The software was developed in Python to integrate all components and to enable data acquisition on the PC. The equipment was adequate for detecting the irrigation start and end times, as well as for measuring the irrigation dose, volume of drainage solution, irrigation period, drainage period, and evapotranspiration, while the transpiration and evaporation models predicted plant water needs. After each irrigation, the scale readings were utilized to i) assess the accuracy of the model prediction and ii) recalibrate the model when the accuracy was less than 10% while also taking into account the environmental parameters. Using the aforementioned technique to develop an irrigation schedule, the model's regression coefficient (R²) was increased from 0.89 to 0.99, resulting in a 0.35 L m⁻² day⁻¹ saving of nutrient solution. In conclusion, the use of weight sensor measurements can significantly improve transpiration model prediction through a frequent recalibration of the transpiration model.