The role of machine intelligence in photogrammetric 3D modeling-an overview and perspectives

The process of modern photogrammetry converts images and/or LiDAR data into usable 2D/3D/4D products.The photogrammetric industry offers engineering-grade hardware and software components for various applications.While some components of the data processing pipeline work already automatically,there is still substantial manual involvement required in order to obtain reliable and high-quality results.The recent development of machine learning techniques has attracted a great attention in its potential to address complex tasks that traditionally require manual inputs.It is therefore worth revisiting the role and existing efforts of machine learning techniques in the field of photogrammetry,as well as its neighboring field computer vision.This paper provides an overview of the state-of-the-art efforts in machine learning in bringing the automated and‘intelligent’component to photogrammetry,computer vision and(to a lesser degree)to remote sensing.We will primarily cover the relevant efforts following a typical 3D photogrammetric processing pipeline:(1)data acquisition(2)georeferencing/interest point matching(3)Digital Surface Model generation(4)semantic interpretations,followed by conclusions and our insights.

Everything moves:The rapid changes in photogrammetry and remote sensing

Changes in technology are coming at an ever increasing pace.This holds for photogrammetry and remote sensing as well.“Everything moves”-this is why I chose this topic to shed some light on some of the recent developments.Naturally,this undertaking can never be complete in the sense of covering all developments in Photogrammetry and Remote Sensing.Besides,the impact of Deep Learning in photogrammetry is not mentioned in this paper.This is a very personal account.People may not agree with some of my findings,but this is in the nature of science.In any case,this contribution is meant as a tribute to Gottfried’s successful lifelong work.It is not a scientific paper in the traditional sense but rather a collection of thoughts that emerged over the 50 years of my professional career.It is also meant for an audience who has not necessarily a deep photogrammetric expert know-how.

FORSAT:a 3D forest monitoring system for cover mapping and volumetric 3D change detection

A 3D forest monitoring system,called FORSAT(a satellite very high resolution image processing platform for forest assessment),was developed for the extraction of 3D geometric forest information from very high resolution(VHR)satellite imagery and the automatic 3D change detection.FORSAT is composed of two complementary tasks:(1)the geometric and radiometric processing of satellite optical imagery and digital surface model(DSM)reconstruction by using a precise and robust image matching approach specially designed for VHR satellite imagery,(2)3D surface comparison for change detection.It allows the users to import DSMs,align them using an advanced 3D surface matching approach and calculate the 3D differences and volume changes(together with precision values)between epochs.FORSAT is a single source and flexible forest information solution,allowing expert and non-expert remote sensing users to monitor forests in three and four(time)dimensions.The geometric resolution and thematic content of VHR optical imagery are sufficient for many forest information needs such as deforestation,clear-cut and fire severity mapping.The capacity and benefits of FORSAT,as a forest information system contributing to the sustainable forest management,have been tested and validated in case studies located in Austria,Switzerland and Spain.

SMART Cities: The need for spatial intelligence

For the first time in human history,more than half of the global population lives in urban areas.This will increase to 70%by 2050.Shanghai’s population has almost doubled in a decade,from less than 13 million residents in 2000 to an estimated 23 million today,and by 2050 it is expected to exceed 50 million.Cities cover just 2%of the Earth’s surface yet consume about 75%of the world’s resources.So it becomes obvious that cities are the key element when coping with climate change and reduction in the use of resources.Since city growth can hardly be avoided,one must be able to cope with its consequences.Here it is essential that harmony exists or is generated among the spatial,social,economical and environmental aspects of a city and between their inhabitants.This harmony hinges on three key pillars:Earth environment,economic development and social equity.These pillars are balanced through sustainability.In this context the concept of a SMART city has emerged.Usually,“smartness”is expressed by its 6-axes model:smart economy,mobility,environment,people,living and governance.Only if all these elements are in balance a city can fulfil its request for sustainability and quality of life.In other words,a city can be called“smart”if investments in human and social capital and traditional(transport)and modern information and communication technologies,information and communication infrastructure will fuel sustainable economic development,a high quality of life,with a wise management of natural resources,through participatory governance.

Geoinformatics education and outreach:looking forward

Geoinformatics education is a key factor for sustainable development of geo-spatial sciences and industries.There have been a variety of educational activities focusing on education and training,technology transfer,and capability building in photogrammetry,remote sensing,and spatial information science,together known as Geoinformatics.Geoinformatics education is an essential mission and even determinant in the ISPRS society.The paper discusses key issues in Geoinformatics education.It reviews educational activities from the ISPRS perspective and summarizes lessons learned from these actions.A vision towards future trends of Geoinformatics education in the ISPRS is provided.