Cloud Mask Intercomparison eXercise (CMIX): An evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2

Cloud cover is a major limiting factor in exploiting time-series data acquired by optical spaceborne remote sensing sensors. Multiple methods have been developed to address the problem of cloud detection in satellite imagery and a number of cloud masking algorithms have been developed for optical sensors but very few studies have carried out quantitative intercomparison of state-of-the-art methods in this domain. This paper summarizes results of the first Cloud Masking Intercomparison eXercise (CMIX) conducted within the Committee Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). CEOS is the forum for space agency coordination and cooperation on Earth observations, with activities organized under working groups. CMIX, as one such activity, is an international collaborative effort aimed at intercomparing cloud detection algorithms for moderate-spatial resolution (10–30 m) spaceborne optical sensors. The focus of CMIX is on open and free imagery acquired by the Landsat 8 (NASA/USGS) and Sentinel-2 (ESA) missions. Ten algorithms developed by nine teams from fourteen different organizations representing universities, research centers and industry, as well as space agencies (CNES, ESA, DLR, and NASA), are evaluated within the CMIX. Those algorithms vary in their approach and concepts utilized which were based on various spectral properties, spatial and temporal features, as well as machine learning methods. Algorithm outputs are evaluated against existing reference cloud mask datasets. Those datasets vary in sampling methods, geographical distribution, sample unit (points, polygons, full image labels), and generation approaches (experts, machine learning, sky images). Overall, the performance of algorithms varied depending on the reference dataset, which can be attributed to differences in how the reference datasets were produced. The algorithms were in good agreement for thick cloud detection, which were opaque and had lower uncertainties in their identification, in contrast to thin/semi-transparent clouds detection. Not only did CMIX allow identification of strengths and weaknesses of existing algorithms and potential areas of improvements, but also the problems associated with the existing reference datasets. The paper concludes with recommendations on generating new reference datasets, metrics, and an analysis framework to be further exploited and additional input datasets to be considered by future CMIX activities

Online benchmark environment for multi-agent reinforcement learning

Zmožnost delovanja (in zmagovanja) v igrah se pri umetni inteligenci pogosto uporablja kot pokazatelj oz. merilo splošnejše sposobnosti. S stopnjevanjem izzivov pa so zaradi tehničnih ovir odmevni podvigi primorani sklepati kompromise - vmesniki simulacijskih okolij so lahko za umetne agente neskladno prirejeni, kar vzbuja negotovosti v primerjavah z ljudmi. Pregled izbranih del na področju globokega spodbujevalnega učenja v realnočasnih strateških igrah poudarja potrebo po novem merilnem okolju, ki z omogočanjem enakovrednejših vmesnikov bolje izpostavlja vlogo strateških elementov in je hkrati primerno za poskuse na porazdeljenih sistemih. Slednje je izvedeno kot skupinska tekmovalna igra, v opisu katere se obravnavajo določeni tehnični in teoretični problemi na primerih posnemovalnega in spodbujevalnega učenja.Capability of acting (and winning) in games is often used in artifcial intelligence as an indicator or measure of more general ability. However, as challenges escalate, notable efforts are forced to compromise due to technical limitations - interfaces of simulated environments can be inconsistently adapted for artifcial agents, which induces uncertainty in comparisons with humans. Review of select works in the feld of deep reinforcement learning in real-time strategy games highlights necessity for a new benchmark environment, which better emphasises the role of strategic elements by enabling more equivalent interfaces and is also suitable for experiments on distributed systems. The latter is realised as a team-based competitive game, in description of which specifc technical and theoretical problems are examined on the cases of imitation and reinforcement learning

Miniature mobile robot detection using an ultra-low resolution time-of-flight sensor

Miniature mobile robots in multi-robotic systems require reliable environmental perception for successful navigation, especially when operating in the real-world environment. One of the sensors that have recently become accessible in microrobotics due to their size and cost-effectiveness is a multi-zone time-of-flight (ToF) sensor. In this research, object classification using a convolutional neural network (CNN) based on an ultra-low resolution ToF sensor is implemented on a miniature mobile robot to distinguish the robot from other objects. The main contribution of this work is an accurate classification system implemented on low resolution, low processing power and low power consumption hardware. The developed system consists of a VL53L5CX ToF sensor with an 8x8 depth image and a low-power RP2040 microcontroller. The classification system is based on a customised CNN architecture to determine the presence of a miniature mobile robot within the observed terrain, primarily characterized by sand and rocks. The developed system trained on a custom dataset can detect a mobile robot with an accuracy of 91.8% when deployed on a microcontroller. The model implementation requires 7 kB of RAM, has an inference time of 34 ms, and an energy consumption during inference of 3.685 mJ

CMIX: Cloud Mask Intercomparison eXercise

Cloud cover is a major limiting factor in exploiting time-series data acquired by optical spaceborne remote sensing sensors. Multiple methods have been developed to address the problem of cloud detection in satellite imagery and a number of cloud masks have been developed for optical sensors but very few studies have carried out quantitative intercomparison of state-of-the-art methods in this domain. Here, we summarize results of the first Cloud Masking Intercomparison eXercise (CMIX) conducted within the Committee Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). CEOS is the forum for space agency coordination and cooperation on Earth observations, with activities organized under working groups. CMIX, as one such activity, is an international collaborative effort aimed at intercomparing cloud detection algorithms for moderate-spatial resolution (10-30 m) spaceborne optical sensors. The focus of CMIX is on open and free imagery acquired by the Landsat 8 (NASA/USGS) and Sentinel-2 (ESA) missions. Ten algorithms developed by nine teams from fourteen different organizations representing universities, research centers and industry, as well as space agencies (CNES, ESA, DLR, and NASA), were evaluated within the CMIX. Those algorithms varied in their approach and concepts utilized which were based on various spectral properties, spatial and temporal features, as well as machine learning methods. Algorithm outputs were evaluated against existing reference cloud mask datasets. Those datasets varied in sampling methods, geographical distribution, sample unit (points, polygons, or full image labels), and generation approach (experts annotations, machine learning, or sky images). Overall, the performance of algorithms varied depending on the reference dataset, which can be attributed to differences in cloud definitions used when producing the reference datasets. Average overall accuracy (across algorithms) varied 80.0±5.3% to 89.4±2.4% for Sentinel-2, and 79.8±7.1% to 97.6±0.8% for Landsat 8, depending on the reference dataset. An overall accuracy of 90% yields half the errors than an overall accuracy of 80%. The study identified algorithms that provided a balance between commission and omission errors, as well as algorithms, which are cloud conservative (high user’s accuracy) and non-cloud (clear) conservative (high producer’s accuracy). With repetitive observations like those of Sentinel-2, it seems reasonable to favor non-cloud conservative approaches, with maybe the exception of very cloudy regions where every cloud free observation is critical. When thin/semi-transparent clouds were not considered in the reference datasets algorithms’ performance generally improved: overall accuracy values increased by +1.5% to 7.4%. It should be noted though that these clouds are commonly occurring and are often present in optical imagery. Within CMIX, we also developed recommendations for further activities, which include provision of a quantitative definition for clouds (targeting moderate spatial resolution imagery by Landsat 8 and Sentinel-2), generation of new reference datasets, and expansion of the analysis framework (for example, multi-temporal analysis and application-driven validation). Such intercomparison studies will hopefully help the community to improve the algorithms and move towards standardization of cloud masking. Given the importance of cloud masking in optical satellite imagery we encourage CEOS to continue the CMIX activities